11xxx 022 Website Dar es Salaam (Dar) (from: دار السلام Dār as-Salām, 'the house of peace'; formerly Mzizima) is the former capital as well as the most populous in and a regionally important economic centre. Located on the, the is one of the fastest growing cities in the world.
Until 1974, Dar es Salaam served as ’s capital, at which point the capital city commenced transferring to, which was officially completed in 1996. However, as of 2018, it continues to remain a focus of central government bureaucracy, although this is in the process of fully moving to. In addition, it is Tanzania's most prominent city in arts, fashion, media, music, film and television and a leading financial centre. The city is the leading arrival and departure point for most tourists who visit Tanzania, including the national parks for safaris and the islands of. Dar es Salaam is also the largest and most populous -speaking city in the world. The Dar es Salaam cenotaph along Sokoine Drive. In the 19th century, Mzizima ( for 'healthy town') was a coastal fishing on the periphery of routes.
In 1865 or 1866, Sultan began building a new very close to Mzizima and named it Dar es Salaam. The name is commonly translated as 'abode/home of peace', based on the Arabic dar ('house'), and the Arabic es salaam ('of peace'). Dar es Salaam fell into decline after Majid's death in 1870, but was revived in 1887 when the established a station there. The 's growth was facilitated by its role as the administrative and commercial centre of and industrial expansion resulting from the construction of the in the early 1900s. Was captured by the British during and became, with Dar es Salaam remaining the administrative and commercial centre. Under British, separate European (e.g., ) and African (e.g., and ) areas developed at a distance from the centre.
The 's population also included a large number of, many of whom. After, Dar es Salaam experienced a period of rapid growth. Political developments, including the formation and growth of the, led to attaining independence from colonial rule in December 1961.
Dar es Salaam continued to serve as its capital, even when in 1964 Tanganyika and merged to form. In 1973, however, provisions were made to relocate the capital to, a more centrally located in the interior. The relocation process has not yet been completed, and Dar es Salaam remains 's primary. In 1967, the declared the policy, that set Tanzania into a socialist path. The move slowed down the potential growth of the city as the government encouraged people not to move in cities but stay in socialist villages. But by the 1980s the policy proved to be a failure into combating increasing poverty, hunger, and delayed development that Tanzania faced.
This led to the 1980s liberalization policy that virtually ended socialism and its spirit within the Tanzania's government. Until the late 1990s, Dar es Salaam was not put into the same category as Africa's leading cities like,. But the 2000s decade became the turning point as the city experienced one of Africa's fastest urbanization rates as businesses were opened and prospered, growth in the construction sector with multi-storey building, bridges and roads, headquartered in the city started to run more properthe expanded, and the proved to be the most important in Tanzania and prominent for entrepot trade with landlocked countries like eastern,. The CBD skyline hosts tall buildings, among them the 35-floor PSPF Tower, finished in 2015, and the (TPA) Tower, currently under construction. Geography. Further information: and Dar es Salaam Region is divided into five administrative districts.
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All five are governed as municipal councils, and so all of the city's suburbs or wards are affiliated with them. The regional commissioner is. Districts of Dar es Salaam Region District Population (2012) Area km² 1,220,611 210 1,775,049 527 1,368,881 656 NA NA NA NA Dar es Salaam Region 4,364,541 1,393 Kinondoni Kinondoni is the most populated amongst the districts, with half of the city's population residing within it. It is also home to high-income suburbs.
These include. The Coco Beach public beach during the daytime, on the western shores of the. Masaki, and are the high-income suburbs located along the central beach. During the Colonial Era, they were the major European suburbs of the city. Now diplomats and expatriates reside in these areas.
Oysterbay Beach, also known as Coco Beach, is the only white sandy beach east of Kinondoni. Mikocheni and Regent Estate are also suburbs within the district. According to the 2012 census, the Mikocheni ward had a population of 32,947.: 75. is a peninsula to the northeast of the city center. It is home to expatriates from the United Kingdom and other western countries.
Msasani contains a mixture of traditional shops and western-oriented resorts and stores. Mbezi Beach is the beachfront suburb located along the northern Dar es Salaam Beach. It contains several tourist hotels, residences and further more a kite surfing area by Upepo Avenue. Sinza, Kijitonyama, Magomeni, and are more ethnically mixed than the areas above. These are located west of Dar es Salaam's central business district. Tandale, Mwananyamala-Kisiwani and Kigogo are considered low-income neighborhoods characterized by poor settlement planning, low quality housing and social services.
The Askari Monument along Samora Avenue marks the exact centre of Dar es Salaam, in the Ilala district. Ilala is the administrative district of Dar es Salaam where almost all government offices and ministries are housed. The Central Business District (locally called 'Posta') is located in this district. It is the transportation hub of the city, as the, Central Railway Station and Tazara Railway Station are all within the district boundaries. The residential areas are mainly middle to high-income, and some of these are:. Upanga & had the highest concentration of Asian communities within Dar es Salaam, with residents of Indian and Arabian descent.
These areas contain colonial houses and mansions built in, Arabic and European styles. Upanga contains. is the shopping district of the city. Shops, bazaars and merchants dot the streets, selling products from foodstuffs to hardware materials.
The Kariakoo Market, which is the largest, contains the only underground section of the city. It is the major supply point of the food consumed by all the residents of Dar es Salaam. Tabata, Segerea and Ukonga are located a bit farther west from the city center. They are becoming busier in terms of business and entertainment, which has caused serious traffic congestion. Ilala is among the middle-income suburbs very near to the city center, and is marked by the.
It suffers from activity. Temeke. The Nyerere Bridge in Kigamboni that connects Kigamboni suburbs with Dar es Salaam CBD through. Temeke is the industrial district of the city, where the manufacturing centers (heavy and light industry) are located. The, which is the largest in the country, is found east of Temeke.
Temeke is believed to have the largest concentration of low-income residents due to industry. Port officials, military and police officers live there.
Kurasini located on the Dar es Salaam Harbour, is the home of the Dar es Salaam Port, The Police College, Mgulani Police Barracks and the Grounds. Thus, the main residents are police officers and port officials. Chang'ombe is one of the only higher income areas in Temeke. It has maintained this status due to occupation by African high colonial officers and some industry owners from the colonial era.
Chang'ombe is the home of the Dar es Salaam University College of Education, the National Stadium and Uhuru Stadium. Temeke, Mtoni and Tandika are middle to low-income suburbs. Mbagala and Kijichi are middle to low-income suburbs. Mbagala is the largest suburb in the whole district, and is also considered a slum. Ubungo The Ubungo terminal serves as a transportation link to most large Dar es Salaam urban nodes.
The commuter rail runs from there to the city centre, with ten along the route. Railway tracks over Kurasini road, Dar es Salaam Kigamboni (South Beach), a beachfront suburb on a peninsula, is home to an economically diverse population. Access to the suburb is mainly by ferry, although the provides an alternative.
Climate Due to close proximity to the and the warm Indian Ocean, the city experiences tropical climatic conditions, typified by hot and humid weather throughout much of the year. It has a (: Aw).
Annual rainfall is approximately 1,100 mm (43 in), and in a normal year there are two rainy seasons: 'the long rains' in April and May and 'the short rains' in November and December. The Julius Nyerere International Convention Centre in Dar es Salaam 'In 1949 the town became a municipality.with four honourable nominated Town Councillors who elected a Mayor.' 'Until June 1996, Dar es Salaam was managed by the Dar es Salaam City Council.the highest policy-making body in the city.' As of 2017 serves as the commissioner of.
Globalization As any growing city Dar es Salaam is the city in Tanzania to which villagers flock for better opportunities. Westerners and Asians are also settling in Dar es Salaam, and the movement of foreigners has put a good work load on the relevant government body for developing better policies to accommodate the growing and the diverse population of the Dar es salaam together with its suburbs.
Population Dar es Salaam is the most populous city in Tanzania. With a population increase of 5.6 percent per year from 2002 to 2012, it is the third-fastest-growing city in Africa, after and, and the ninth-fastest-growing in the world. The is expected to reach 5.12 million by 2020 and predicted to be as high as 76 million by the year 2100, making it the third largest city on earth (after Lagos and Kinshasa), by 2100. According to the 2012 national census, the region had a population of 4,364,541, which was much higher than the pre-census projection of 3,270,255.: page 2 For 2002–2012, the region's 5.6 percent average annual population growth rate was the highest in the country.: page 4 It was also the most densely populated region with 3,133 people per square kilometer.: page 6 The sprawling suburbs furthest from the city centre are generally populated by Tanzanians of African descent, with the exception of, where there is a large population of foreign expatriates. The edges of Dar es Salaam are spreading rapidly, severely taxing the transportation network (which aside from ferries, lacks any kind of mass transit facilities) and raising the prospect of future urban overcrowding. Year Population 1925: 30,000 1948: 69,000 1957: 129,000 1972: 396,000 2005: 2,456,100 2012: 4,364,541 2025: 5,690,000 (projection) Economy and infrastructure. The busy market during the daytime.
Dar es Salaam is Tanzania's most important city for both business and government. The city contains high concentrations of trade and other services and manufacturing compared to other parts of Tanzania, which has about 80 percent of its population in rural areas. Downtown includes small businesses, many of which are run by traders and proprietors whose families originated from the Middle East and the Indian sub-continent—areas of the world with which the settlements of the Tanzanian coast have had long-standing trading relations. The Dar es Salaam Central Business District is made up of, and areas is Tanzania's largest city CBD.
All three areas making up the downtown are found in the Ilala district. Kivukoni has the city's important fish market, the Magogoni fish market. Kivukoni also is the place where the Tanzania's central bank, The is located, so is the. Kisutu has businesses and offices and is the location of Dar es Salaam central railway station, the PSPF Towers and the TPA tower.
An aerial view of Dar es Salaam facing towards the creek. Dar es Salaam has a problem with slums. According to a estimate, 70 percent of the city's population lives in informal settlements. The poorer residents crowd into downtown areas or large slums, many without running water or basic services. The more wealthy live in beachside mansions in the city's northern districts.
Challenges facing the Dar es Salaam Economy and Infrastructure 05 July 2018 Dar es Salaam has had a major construction boom. The with more than 35 stories is the tallest building in the city and the country. Dar es Salaam has major infrastructural challenges, including an outdated transport system and occasional power rationing. Financial Services The (DSE) being the country's first and most important stock exchange market.4 Retail Dar es Salaam hosts the shopping Mall. Transportation.
The Dar Rapid Transit (DART) is a bus-based mass transit system connecting the suburbs of Dar es Salaam to the central business district. Dar es Salaam on a natural harbour on the, is one of the hub of the as the main railways and several highways originate in or near the city to serve convenient means of transportation for commuters. Local public transport The most common form of transport in Dar es Salaam are the public buses, called, which are often found at the major bus terminals of. Since the introduction of motorcycle transit business known as 'Bodaboda', most of the people prefer this type of transportation which allows them to get into the city faster compared to the minibuses which face a lot of traffic. Other types of transport include motorcycles. Bus The government has been introducing a bus rapid transport or metro bus system under the meaning 'mwendo kasi' in Ki. The metro buses are managed by UDA (Usafiri Dar es Salaam).
The system Phase 1 is completed and already in operation by the Dar es Salaam Rapid Transit Agency, a government-private sector entity, and began operation on 10 May 2016. It is branded as UDA-RT (Usafiri Dar es Salaam Rapid Transit). The first section runs between Kimara in the northwest to on the northern headland of the harbour. Phase 1 was funded by the, and the Tanzanian government. Maritime Transport Port. The MV Kigamboni ferries running between south east to north west in south east Dar es Salaam during the daytime. MV Kigamboni ferries are running between south east of and north west of in Dar es Salaam.
Railway Dar es Salaam commuter rail Travel to Urban and sub urban parts of Dar es Salaam is provided by the. Intra City Railway operates the from Dar es Salaam to International Railway The city also hosts the head office of Tanzania Zambia Railways Authority built in the late 1960s to early 1970s. The main terminal is located west of Dar es Salaam's central business district in north Yombo Vituka along Nelson Mandela road.
The connects Dar es Salaam to. The main gate of Nyumba ya Sanaa, with decorations by Tanzanian sculptor George Lilanga. Dar es Salaam (and specifically the area of Oyster Bay) is home to the brightly coloured and tourist-oriented painting style. The Tingatinga Arts Cooperative Society is a cultural centre, workshop and shop dedicated to Tanzanian art, showcasing and promoting Tanzanian craftmanship. Prominent Tanzanian sculptor has donated some of his works to the centre, including decorations of the building's main entrance.
A traditional African dance in Dar es Salaam. The music scene in Dar es Salaam is divided between several styles.
The longest standing style is live dance music bands such as and Malaika Musical Band as examples. Which was traditionally strong in has also found a niche.
However, it remains small compared both to dance music and ', a broad category that represents the Tanzanian take on Hip Hop and R&B, which has quickly become the most popular locally produced music. Traditional music, which locally is used to refer to tribal music is still performed but typically only on family oriented occasions such as weddings. Recently there has been development of another music niche, a taste that is rising and to be prominent as BongoFlava known as Singeli with star singers such as Msaga Sumu and Man Fongo.
This scene is also present. In the 1970s, the Ministry of National Youth Culture aimed to create a national culture, which stressed the importance of music. Dar es Salaam became the music center in Tanzania, with the local radio showcasing new bands and dominating the music and cultural scene. With this ujamaa, or family, mentality governing culture and music a unified people’s culture was created, leading to the rise of hip hop music. Throughout the years, the radio in Dar es Salaam has played a major role in the dissemination of music because many people don’t have television and cassettes are used over CDs.
Cuisines. This section does not any. Unsourced material may be challenged. ( June 2013) Due in part to the growth of the expatriate community and the increasing importance of tourism, the number of restaurants serving international cuisines has risen rapidly. The city offers a diversity of cuisine, ranging from traditional Tanzanian Barbecue-style options, such as Nyama Choma (Roasted —served with or ) and Mishkaki (—usually barbecued and served with, hot, and on the side), as well as the long-established traditional and, to options from all corners of the globe, including,.
People who are looking for a light meal or a snack and prefer neither fast food nor a meal from the traditional restaurants buy their food from street vendors, who usually sell good food and snacks at low prices. ( sambusas) with coconut chutney are the most common snack street food items within the city, as the area is largely influenced by the fresh food products and spices imported from. A traditional Tanzanian hut on display at the Makumbusho Village Museum on Bagamoyo road.
Dar es Salaam has two of the five museums comprising the consortium, namely the National Museum proper and the Makumbusho Cultural Centre & Village Museum. The National Museum is dedicated to the history of; most notably, it exhibits some of the bones of that were among the findings of at.
The Makumbusho Cultural Centre & Village Museum, located in the outskirts of the city on the road to, showcases traditional from 16 different ethnic groups. There are also examples of traditional cultivations, and traditional music and dance shows are held daily.
In 2016, there was a breakthrough discovery in Northern Tanzania by a scientist, from the University of Dar es Salaam, of footprints thought to be of a hominid that predates Homo sapiens. Close to the National Museum are also the, with tropical plants and trees. There are beaches on the peninsula north of Dar es Salaam and in to the south. Trips to the nearby islands of the are a popular daytrip from the city and a spot for snorkeling, swimming and sunbathing. Can be reached by boat from the Msasani Slipway.
Sports Stadium Dar es Salaam is the sports center of Tanzania. Dar es Salaam hosts the second largest stadium in and , which can accommodate up to 60,000 people. An aerial view of the National Main Stadium with the Kurasini creek in the background.
Football (Soccer) The hosts Dar es Salaam's, and other Tanzanian football clubs, and international matches. There is a proposal to build a new stadium in, much bigger in capacity than the present one in Dar es Salaam by the government as a donation from the Moroccan Kingdom. Apart from the National Stadium, Dar es Salaam is home to the (used mainly for local tournaments and political gatherings), Karume Memorial Stadium (the home of the ). The stadium is situated west of. Golf The Gymkhana Golf Courses located north west of the area (between the city centre looking on to the shores of the in the east and Barack Obama Drive), also has, and a. Outside of the metropolitan districts, there is the (located in the Lugalo Military Barracks). Acrobatics Dar es Salaam's Mama Africa school, founded in 2003, is known for training some of Africa's finest.
Squash Dar es Salaam's Union Sports Club hosts a single indoor squash court with a referees viewing gallery within the club grounds. The club has a yearly Squash tournament once a year during the muslim month of ramadhan. Darts Dar es Salaam's Union Sports Club hosts a single room Darts room.The club has a yearly Darts tournament once a year during the muslim month of ramadhan.
Table Tennis Dar es Salaam's Union Sports Club hosts a single room Table Tennis. The club has a yearly Table Tennis tournament once a year during the muslim month of ramadhan. Scrabble Dar es Salaam's Union Sports Club hosts an under the sky outdoor scrabble tournaments within the club grounds once a year during the muslim month of ramadhan. Swimming Dar es Salaam hosts numerous outdoor swimming clubs and also swimming opportunities offered to people for leisure on the sandy beaches of the Indian Ocean being a coastal city. Media Newspapers Newspapers in Dar es Salaam are often sold by people prowling through stationary traffic at road intersections.
English-language ones, with online presences, include The Citizen and The Guardian and the dailies, Tanzania Daima and Mwananchi. Business Times is the only financial and economic newspaper in the city. It was established in 1988 and became the first private newspaper in Tanzania. Business Times owns Majira, another Kiswahili newspaper. Television stations Dar es Salaam is home to ITV, Channel Ten Television Station formerly known as Dar es Salaam Television (DTV) and Azam TV, a subscription-based service from the Azam group of companies.
Ayo TV, a television station, is also based in, Dar es Salaam, as is the. Internet access Installation of a in 2009 has, in theory, made Internet access much more readily available in Dar in particular and in East Africa in general. However, roll-out to end-users is slow, partly because of spotty telephone line coverage at the moment provided by the, partly due to the substantial prices and long contracts demanded for purchase of bandwidth for small ISPs. Mobile-telephone access to the Internet via and is still relatively expensive. Is making its way through major cities and towns as of 2015 with plans to go countrywide in the advanced planning stages.
Are found in the city centre and free wifi hotspots in various government and non government institutions as well as public transport. The expressed aim of the is to enable East Africa to develop economically through increased online trading. Education. The Nkrumah Hall at the. Dar es Salaam is the educational centre of. The city is home to several institutions of. Below are 8 as follows: Universities.
The is the oldest and 2nd largest public university in Tanzania after the. It is located in the western part of the city in north-east, occupying 1,625 acres (6.58 km 2) on the observation hill, 13 km (8 mi) from the city centre. The university has 16,400 undergraduate and 2,700 postgraduate students. The (ARU) was established on 1 July 1996 after transforming the former University College of Lands and Architectural Studies (UCLAS) which was then a Constituent College of the. Historically, Ardhi University, dates back to 1956 when it started as Surveying Training School offering land surveying technician certificate courses at the present location of Mgulani Salvation Army Camp in Dar es Salaam. In 1958, the School was moved to the present location (i.e. The Observation Hill).
In 1972, the School was transformed to Ardhi Institute. Then the Institute offered two-year diploma programmes in the fields of Land Surveying and Land Management and Valuation. In the same year, a three-year Diploma program in Urban and Rural Planning was introduced.
Later in 1975, all the three-year diploma programmes were upgraded to Advanced Diplomas. The Building Design and Building Economics courses started in 1976 and 1978 respectively. As pointed out earlier, in 1996 Ardhi Institute was affiliated to the as a Constituent College of the University; subsequently its name changed to the University College of Lands and Architectural Studies (UCLAS). Within ten (10) years, UCLAS increased the number of academic programmes from six (6) to thirty nine (39). The programmes ranging from diplomas to PhDs were offered by two faculties, that is, the Faculty of Architecture and Planning (FAP) and the Faculty of Lands and Environmental Engineering (FLEE). As a result of these changes, student enrollment increased from only 400 in 1996 to about 1,400 in 2007.
At the same time, the number of academic staff with PhD increased from 3 in 1996 to over 60 in 2012. At present there are over 80 PhD holders who have graduated from over 25 universities worldwide. In 2007 Ardhi University came into being following the signing of the Ardhi University Charter by His Excellency the President of The United Republic of.
Concurrently, the structure and number of programmes and academic units has increased significantly. At present, the University comprises four Schools, one Institute and several centres. Schools includes; The School of Architecture,Construction Economics and Management(SACEM), The School of Earth Sciences, Real Estates, Business and Informatics(SERBI), The School of Environmental Science and Technology(SEST), and The School of Spatial Planning and Social Science(SSPSS), alongside Institute of Human Settlements Studies (IHSS). The university offers Undergraduate and Postgraduate studies with Postgraduate, Bachelors, Masters and PhD degrees in various disciplines. The has two campuses; Muhimbili Campus and Mloganzila Campus. Muhimbili Campus is situated in Ilala Municipality, in Upanga along United Nations Road. Mloganzila Campus occupies 3,800 acres (15 km 2) and is located 3 km (2 mi) off Dar es Salaam-Morogoro highway, 25 km (16 mi) from Dar es Salaam.
The is a fully fledged and accredited public institution of higher learning, running programmes leading to certificates, diplomas, undergraduate and postgraduate qualifications. Since it was founded, the university has enrolled students from, and most of. As of 2008, the total enrollment at the university was 44,099, the majority of whom were.
The is a private institution located on plot No. 322 Regent Estate in the Mikocheni area, some 7-km from the Dar es Salaam City centre, off Ali Hassan Mwinyi and Old Bagamoyo roads. The is a privately owned institute of higher education institution operating in Dar es Salaam. The —began operations in 2009. The University Centre is situated on 60 acres (240,000 m 2) of land in the Gongo la Mboto area, Ilala District, 7 km (4 mi) from Mwalimu Julius Nyerere International Airport along Pugu Road.
The National institute of Transport Notable people Below is a list of nineteen notable people who lived in Dar es Salaam. This section needs additional citations for. Unsourced material may be challenged and removed. ( September 2014)., London-based architect, born in Dar es Salaam in 1966. (1914–1966), architect in Dar es Salaam 1947–1966., former president of, headed the headquarters in Dar es Salaam where he lived before returning to Mozambique after the country won independence.
He was the first head of state to address the African Union (AU) in Swahili before Swahili became one of the official languages of the AU. He learned the language during the years he spent in Tanzania as a member of FRELIMO and became fluent in it., one of the main leaders in the independence struggle in (renamed after the country won independence) where he served as minister of education and then as minister of foreign affairs before going into exile in Tanzania, lived in Dar es Salaam for decades. He grew up in Tanganyika. He spent his childhood in and in Dar es Salaam and attended primary school and secondary school in (later renamed Tanzania). When he was in boarding school in Dar es Salaam, he lived in the same dormitory with who later became vice president of Tanganyika, later Tanzania, after the country won independence. He also attended Secondary School (then known as Upper School) with who became Tanganyika's first minister of defence and foreign affairs after the country became independent.
Chiume graduated from University College in and went to teach at Alliance Secondary School in, Tanganyika. After he left Malawi in 1964 to go into exile in Tanzania (1964 - 1994), he went to live in Dar es Salaam again. He also worked in Dar es Salaam for many years as a journalist at The Nationalist, a newspaper of the ruling party TANU, together with who was then the editor and who later became president of Tanzania., writer, lived in Dar es Salaam 1934–1939., scientist, and primatologist., actress, TV Show., actress, born in Dar es Salaam in 1971., player and second national coach was born in Dar es Salaam., president of Uganda, lived in Dar es Salaam for many years, first as a student and later as a political refugee.
He attended the University of Dar es Salaam where he studied economics and political science. One of his professors was, a scholar from who wrote the book How Europe Underdeveloped Africa, in the early seventies when he was teaching at the University of Dar es Salaam., a prominent Tanzanian author. He attended school in Dar es Salaam and worked in the same city as an information officer at the Ministry of Information and Broadcasting and as a news reporter at the Daily News before going to the United States for further studies. He later became an author of non-fiction books about Africa and the and an Africanist scholar., a Tanzanian diplomat, lawyer and author of a number of books about African politics and economics who once served as secretary-general of the East African Community (EAC)., fashion model., historian, political activist and scholar. He was the author of.
He taught at the University of Dar es Salaam in Tanzania from 1966 to 1967 and later at his alma mater, the University of the West Indies, Mona campus, Kingston, Jamaica. In 1969, Rodney returned to the University of Dar es Salaam where he served as a professor of history until 1974 before going back to Guyana where he was assassinated in June 1980., Tanzanian economist, author and professor of economics at the University of Dar es Salaam. He worked at the United Nations and served as an economic adviser to Tanzania's first president,., a Tanzanian academic and author and one of Africa's experts on constitutional law and development issues. He served as professor of law at the University of Dar es Salaam for many years and was the first to hold the Mwalimu Julius Nyerere Research Chair in Pan-African Studies at the university.
He also taught at a number of universities around the world., basketball centre., commander of the Army, composed of European German officers and senior non-commissioned officers and native black African, undefeated by the British and South Africans between the 's outbreak in August, 1914 and the in November, 1918., African footballer of the year 2015 for the domestic players. Sister cities Dar es Salaam is with:., Germany., Turkey., China., Iran References. Retrieved 18 November 2014. Retrieved 21 May 2017. ^ May 2, 2013, at the. ^ Brennan, James R.; Burton, Andrew (2007).
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Methodology/Principal Findings To break intra- and inter-individual variance in clinical studies down to three levels–technical, anatomic, and individual–we designed experiments and algorithms to investigate three forms of variances. As a case study, a group of “inter-individual variable genes” were identified to exemplify the influence of underestimated variance on the statistical and biological aspects in identification of differentially expressed genes. Our results showed that inadequate estimation of variance inevitably led to the inclusion of non-statistically significant genes into those listed as significant, thereby interfering with the correct prediction of biological functions. Applying a higher cutoff value of fold changes in the selection of significant genes reduces/eliminates the effects of underestimated variance.
Conclusions/Significance Our data demonstrated that correct variance evaluation is critical in selecting significant genes. If the degree of variance is underestimated, “noisy” genes are falsely identified as differentially expressed genes. These genes are the noise associated with biological interpretation, reducing the biological significance of the gene set. Our results also indicate that applying a higher number of fold change as the selection criteria reduces/eliminates the differences between distinct estimations of variance. Introduction Over the last decade, microarray studies have had a profound impact on transcriptomic research. One particularly important clinical application of microarray technology is the identification of differentially expressed genes, which may serve as biomarkers for the diagnosis and prognostic prediction of tumors or other complex diseases –.
Despite many successful results, some studies have revealed that gene lists derived from similar studies are highly inconsistent –. Numerous investigations have been conducted to evaluate the influence of multiple factors, such as batch effects, dye effects, different platforms –, various experiment designs –, and statistical approaches, regarding microarray results. However, few studies have explored the influence of different sources of variation on the identification of differentially expressed genes from microarray analysis. Researchers have identified two major sources of variance in microarray studies: technical variance and biological variance. All forms of variations influenced by experimental artifacts, such as the quality of RNA, batch effects, and experimental parameters, belong to technical variance. A well-conceived experimental design and execution as well as rigorous statistical analysis can reduce the effects of technical variation. Studies have demonstrated that loop designs are more efficient than reference designs in two color microarrays, and many statistical methods can be used to increase the robustness of microarray data analysis,.
Several studies have concluded that the reproducibility of microarrays could be improved using standardized protocols and carefully designed and controlled experiments,. Biological variance is attributed to specimens, rather than procedures, and can be traced to several sources. Anatomic variance is caused by the heterogeneous distribution of cell types within a tissue specimen collected from a single individual. Individual variance is a result of various genotypes and physiological states.
For variation in genotypes, copy number variations (CNVs), and allele variations, have been shown to influence gene expression levels. Physiological status such as environment factors, disease state, and other variables influence gene expression. Many researchers have reported biological variance in human blood, lung, placenta, retina, and other tissues –. In addition, variations in gene expression have been identified among individuals as well as populations – and species,. However, the effects of applying different levels of variances have not been well addressed. In this study, we used the normal human placenta as a model to evaluate technical, anatomic, and individual variance.
Each of these types of variation should be considered in clinical studies. The “inter-individual variable gene” was used as an example to evaluate the influence of estimating variance on microarray results. We profiled three levels of variance in human clinical studies and addressed the importance of estimating variance on the statistical and biological aspect for microarray studies.
Our data demonstrated that correct variance evaluation is critical in selecting significant genes. Specimen Collection and Processing Eleven normal placental tissues were obtained from 9 healthy individuals who underwent cesarean section without labor pain. This study was approved by the Institutional Review Board of Chang Gung Memorial Hospital (IRB#96-0630B). Inclusion criteria were healthy normotensive term pregnancies with appropriate-for-gestational-age fetuses, who displayed no abnormality on routine ultrasound scans. Exclusion criteria for this study were fetal chromosomal abnormalities, pre- and postnatal malformations or phenotypic anomalies, maternal smoking, maternal obesity, and maternal diseases, such as autoimmune diseases, thrombophilic conditions, and diabetes. The clinical information is summarized in.
Placental specimens were obtained from the same region of the placenta (5 cm away from the site of cord insertion) immediately after delivery. The approximate 2.5-cm thickness of the placental cross section was divided into three equal parts: maternal (includes thin basal plate), middle, and fetal (includes the chorionic plate). We analyzed the middle part of the placental tissues in all of our placental studies,. The tissues were snap frozen in liquid nitrogen and stored at −80°C. The first sample group (G1) comprised samples 1 to 9 of 9 individuals. The second sample group (G2) contained 8–1, 8–2, and 8–3, which were 3 different placental tissues taken from the same individual. The third sample group (G3) consisted of 2 technical replicates, 8–31 and 8–32, using the identical RNA pool.
Microarray experimental design. Three kinds of samples were employed in this study. Individual variance was evaluated using the first sample group (G1), comprising Samples 1 to 9 of nine individuals. The second sample group (G2) was used to evaluate anatomic variance. It contained Samples 8–1, 8–2, and 8–3, taken from three different sections of placenta from the same individual.
The third sample group (G3) consists of two technical replicates, Samples 8–31 and 8–32, using an identical RNA pool for microarray hybridization to evaluate technical variance. The expression of Sample 8–3 could be estimated by the mean expression of Samples 8–31 and 8–32. The mean expression of Samples 8–1, 8–2, and 8–3 represented the expression of Sample 8. Clinical parameter Mean ± SD. Range of this group Reference range Maternal age (y) 32.6±3.7 25 ∼ 36 NA Gravida # 2.6±1.1 1 ∼ 4 NA Para & 1.2±0.8 0 ∼ 2 NA Maternal Hemoglobin (g/dL) 10.8±1.8 8.6 ∼ 13.5 12 ∼ 16 Mean cell volume of RBC (fL) 83±7.6 72 ∼ 92 80 ∼ 100 Systolic blood pressure (mmHg) 117.1±11.4 102 ∼ 136 90 ∼ 140 Diastolic blood pressure (mmHg) 62.4±10.3 50 ∼ 78 50 ∼ 90 Gestational age (week) 38.3±0.9 37 ∼ 39 38 ∼ 40 Neonate body weight (g) 3133±345 2520 ∼ 3580 2430 ∼ 3900 Apgar score% (1 min) 9.0±0.5 8 ∼ 10 7 Apgar score% (5 min) 9.9±0.3 9 ∼ 10 7. RNA Extraction and Microarray Hybridization Total RNA was isolated as previously reported.
Because the purpose of this study was to analyze variance of gene expression that may be commonly encountered at the tissue level, we did not isolate individual cell types from whole tissues. During RNA extraction, 1 ml of Trizol reagent (Life Technologies, Rockville, MD) was added to every 50–100 mg of pulverized frozen placental tissue. Total RNA was isolated using the Trizol reagent (Life Technologies, Rockville, MD). Total RNA was quantified by UV absorption at 260 nm, and RNA quality was examined using the Agilent 2100 bioanalyzer (Agilent technologies, USA). CDNA labeling was conducted using a 3 DNA Array 50™ kit (Genisphere, Hatfield, PA), according to the manufacturer’s protocols.
In brief, 20-µg total RNA was used to perform reverse transcription reaction with SuperScript II RNase H- reverse transcriptase and specific primers (Invitrogen life technologies, USA). All synthesized tagged cDNA targets were then purified using the Microcon YM-30 column (Millipore, USA).
The purified targets and fluorescent 3 DNA reagents were hybridized to the arrays in succession. Arrays were sealed in a homemade hybridization chamber that adapted the design provided in M-Guide (Patrick O.
Brown laboratory, Stanford University, USA). Hybridization was performed at 65°C in a water bath for 16 h, and arrays were washed according to the manufacturer’s protocol. Subsequently, arrays were scanned with GenePix 4100A (Axon Instruments, USA) and images were acquired using GenePix Pro 5.0 software (Axon Instruments, USA). Production of Microarrays We originally ordered 9600 human cDNA clones of the IMAGE library from Incyte Genomics (Palo Alto, Calif, USA) and allowed sequencing at that location. Only 7334 clones passed sequence verification by Incyte Genomics and were shipped to us. Therefore, every clone of this 7334-clone cDNA library had an IMAGE ID, DNA sequences, vector names, and information for PCR primers.
All clones were further amplified by PCR and purified by isopropanol precipitation in 96-well plates. The purified DNAs were resuspended in 3×SSC for spotting. A single microarray slide (CMT-GAPsII, Corning Inc., USA) contains 7334 human cDNA probes in quadruplicate, 10 spike-in genes (SpotReportTM-10 Array Validation System, Stratagene, USA), and one housekeeping gene, β-actin, in 96 replicates. Each array had 32,448 spots. The arrays were post-processed as recommended in the Corning UltraGAPS Coated Slides Instruction Manual. Microarray slides were produced in a well-controlled environment (28±2°C and 48±1% humidity) and stored under desiccation until use. The array system was assembled according to M-Guide (Patrick O.
Brown laboratory, Stanford University, USA) and controlled using ArrayMaker, version 2.5.1 (Joseph DeRisi laboratory, UCSA, USA). A rigorous system commissioning was performed to guarantee the quality of the printed arrays. Before hybridization, the slides were preprocessed according to the instruction manual for the Corning UltraGAPS Coated Slides, including rehydration, snap-dry, UV-crosslinking, baking, and surface blocking. DNAs were UV-crosslinked with 300 mJ/cm2 using the Stratalinker 2400 UV Crosslinker (Stratagene, USA).
Where γ represents the relative labeling efficiency between dyes, λ i is log2 (expression of sample i/mean expression of all samples) for one specific cDNA clone, with, and ε is the random error with mean 0 and variance σ 2. Σ represents the estimated variance for one specific cDNA clone. For each clone, λ i and σ are estimated from the observed data by using the least squares method as. When the data had been processed using the log linear model, 5501 genes could be calculated in the model without singularity. Is estimated. Is estimated.
A further description of the statistical model can be found in. We had developed a Web tool for loop-design microarray data analysis. All of the front-end analyses of our microarray data were conducted using this public available Web tool. The microarray data of this work are MIAME compliant and have been deposited in the GEO of NCBI (accession number: ). Differential Expression and Averaged Fold Change Differential expression is log2 (fold change of 2 samples) for one specific cDNA clone and is denoted as, where x is the index denoting clones and i,j denoting samples. Differential expression profiles in are the histograms of data set S1:, S2:, and S3:, which are the set of all when x runs over all clones and ( i,j) runs over all possible pairs in G1, G2, and G3, respectively. For S1, i and j range from 1 to 9.
For S2, i and j range from 8–1 to 8–3. For S3, i and j are 8–31 and 8–32, respectively. Moreover, averaged fold change is estimated.
To describe the variation of gene expression between samples. The summation runs on every dual-color microarray experiment (represented by an arrow in ), where x is the xth clone, i is for the sample represented by the tail of the arrow, j is for the sample represented by the head of the arrow, and n is the number of sample pair i,j. We used the sampling permutation method to describe the D quantity when considering three levels of variance. D1, D2, and D3 are the results of 10 million times the sampling permutation of and, for taking n data from S1, S2, and S3 at one time. The corresponding p values of the D quantity are determined using the smoothed curve of the probability density in. The criterion of the p value for the statistical test in this study is a false discovery rate (FDR) of 5%.
Functional Enrichment Analysis Gene Ontology (GO)-based functional enrichment analysis is used to measure gene enrichment in annotation terms for the inter-individual variable genes. The significance score in is –log (EASE Score), where the EASE Score is a modified Fisher exact p value obtained by DAVID. The GO terms passed the criteria, EASE Score. The Profiles of 3 Levels of Variance We used a loop design in a microarray analysis of normal placental tissues to investigate technical, anatomic, and individual variance in microarray data. Is a schematic representation of the interwoven loop hybridization design performed in this study. We selected 11 normal placental tissues from 9 women with term pregnancies, who underwent Cesarean section prior to the onset of labor, to avoid variations caused by labor pain.
Microarray data were obtained from 3 sample groups to estimate individual, anatomic, and technical variance. The first sample group (G1) comprised Samples 1 to 9, samples of 9 individuals. The second sample group (G2) contained Sample 8–1, 8–2, and 8–3, which were 3 different placental regions taken from the same individual. The third sample group (G3) consisted of 2 technical replicates, Sample 8–31 and 8–32, obtained from the same RNA pool.
Differential expression profiles in are log (fold change) between samples in 3 sample groups (G1, G2, and G3) and it is the histogram of data series S1, S2, and S3, respectively. These results were presented as distributions of the fold changes of G1, G2, and G3. The results indicate a progressive narrowing of distribution curves from S1 to S3, revealing that individual difference produced a greater degree of relative variability in gene expression than that of the anatomic or technical difference. A test statistic, D quantity, was designed to measure the variation in gene expression between samples.
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Shows the probability density profiles of the D quantity, D1, D2, and D3, representing 3 levels of variability. These profiles were generated by applying permutation methods using the data series S1, S2, and S3, indicating extreme differences in the 3 levels of variance. Case Study: Inter-individual Variable Gene In this study, inter-individual variable genes, of which the expression varies highly between individuals, were used to evaluate the importance of estimating variance. When defining inter-individual variable genes according to D quantity, variations in gene expression were set at a level exceeding that of anatomic variance.
Therefore, when anatomic variance was considered in the significance test, Pa is the p value of the D quantity determined the D2 curve in. When anatomic variance is not considered in the experimental design, technical variance, evaluated by technical replication, is commonly used for the significance test. Pt is the p value of the D quantity determined by technical variance (D3 curve in ). Plots averaged fold change versus 2 corresponding p values (Pa and Pt) for each gene.
When FDR 5% was set as significant, 2 groups of significant genes were obtained. The 2 corresponding cutoff p values are indicated by red arrows in. Averaged fold change was used as another criterion to select inter-individual variable genes. In this study, the 4 averaged fold changes, from 1.2 to 1.5 (the gray arrows in ), served as further criteria for the identification of inter-individual variable genes. The scatter plot of averaged fold change and p values, and the selection of inter-individual variable gene. (a) The scatter plot of log 2 (averaged fold change) and –log (p value). Pa is the p value determined by applying anatomic variance.
Pt is the p value determined by applying technical variance. (b) The enlarged area of the rectangle in (a). The red arrows indicate the corresponding p value of FDR 5%. The gray arrows indicate the averaged fold change criteria: 1.2, 1.3, 1.4, and 1.5. (c) The number of inter-individual variable gene selected by the criteria of FDR 5%, evaluated by technical and anatomic variance (The red arrows in ), and distinct averaged fold changes (The gray arrows in ). We investigated sets of inter-individual variable genes generated according to distinct selection criteria (different averaged fold changes and corresponding p values) to evaluate the effects of differing levels of variance.
Shows the number of significant genes identified using 2 variance criteria, Pt and Pa (the red arrows in ), with different averaged fold changes (the gray arrows in ). When a higher averaged fold change was used, the influence of variance underestimation decreased, as shown by the number of significant genes , but it paid by reducing the number of selected genes.
The difference was eliminated when the cutoff value of averaged fold change was set to greater than 1.3. To evaluate the influence of variance underestimation on biological prediction, the gene lists identified using the criteria in underwent functional enrichment analysis for gene ontology (GO) using DAVID bioinformatics resources 6.7. Among all significant genes listed in, only 11 common GO terms were identified. Shows enrichment analysis results of the 11 GO terms for the significant genes listed when applying anatomic and technical variance with the averaged fold change criteria 1.2 and 1.3. The enrichment results of averaged fold change set at 1.4 and 1.5 were not listed because 2 significant gene lists based on anatomic and technical variance were the same. A significance score was defined as -log ( p value), where the p value represented the significance of each GO term, according to a modified Fisher exact test in DAVID bioinformatics resources 6.7. Hence, a higher significance score represents a higher significance for the result.
For the same GO term, the significance score for the gene set, the p value of which was deduced by applying anatomic variance, was usually higher than that defined by technical variance. This suggests that the lists of significant genes based on technical variance might include “noisy” genes, which reduced the significance of the GO terms. Discussion Even as simple as a single cell, its physiology are governed by various networks, each comprising multiple signaling gene products, which interact through positive and negative feedbacks, as we showed previously. Complexity theory, also known as chaos theory , has been developed to better describe the emergent phenomenon of the cell. Clinical studies investigating the clinical outcomes of individuals often derive results full of noise, which can be further grouped into intra- and inter-individual variance. Therefore, devising analytical approaches to dissect these confounding factors is critical.
In this study, we first collected placental tissues only from carefully selected healthy term pregnancies, avoiding any potential effects from maternal or fetal diseases. For a single organ, different regions may have distinctly specialized functions, leading to variations in gene expression,.
However, this type of variation differs between organs. The anatomic variance identified in this study was the heterogeneous distribution of cell types within a tissue specimen, prevalent in general clinical studies. Therefore, all tissues in this study were obtained from the same regions and same layer of the placenta to avoid biological variance among different regions of the placenta.
We did not isolate fetal trophoblasts from maternal endothelial cells in each placental tissue because we attempted to analyze the intra- and inter-individual variance directly from clinical tissues. To achieve this goal, we used a loop-designed method to increase the statistical power of microarray data analysis. We used a test statistic, D quantity, in this study to describe variations in gene expression between samples. The permutation method was employed to describe the characteristics of the 3 levels of variability. Permutation analysis is frequently adopted for microarray studies – because distributional assumptions (e.g., normal) using microarray data are often questionable. A non-parametric approach considering factors such as non-uniform distributions could exhibit the characteristics of data more appropriately. The profiles shown in illustrate the differences in the 3 levels of variability, demonstrating that the evaluation of the correct variance must be considered in the experimental design to define statistically significant genes.
For the selection of significant genes, the results of phase I of the MicroArray Quality Control (MAQC) project suggest that the inter-platform reproducibility of enriched KEGG pathways and GO terms was markedly increased when fold-change ranking in addition to a non-stringent p value cutoff were used as the selection criteria. Thus, we used a non-stringent p value, FDR 5%, with averaged fold change as the selection criteria. However, the relationship between the stringency of fold change and biological significance remains controversial. We compared the use of 4 averaged fold changes as criteria to identify the common GO terms of all selection criteria. Suggested that the robustness of biological conclusions derived from microarray analysis should be routinely assessed by examining the validity of the conclusions using a range of threshold parameters. Hence, common GO terms are representative functions for inter-individual variable genes.
In this manner, the influence of variance underestimation could be evaluated by using the significant scores of the common GO terms. The significant scores of the canonical pathways had been used to access distinct selection criteria. The identification of inter-individual variable genes through different variance levels demonstrates the importance of estimating variance from the statistical and biological viewpoints. From the statistical aspect, the impact of variance underestimation includes non-statistically significant genes in the gene list.
From the biological aspect, significant scores of GO terms were used to evaluate the gene sets from distinct criteria. Shows a summary of biological evidence for evaluating gene sets with different significance criteria. It also shows that significant gene sets with accurate evaluation of variance provided more accurate biological interpretations. Our results also suggest that applying a higher cutoff point of fold change reduced, or even eliminated, the influence of variance underestimation. This may be a solution to overcome the difficulties associated with the identification of significant genes when the estimation of precise variance has not been considered adequately in the experimental design, although it paid by reducing the number of the final gene list.
This study demonstrated the importance of estimating variance. Different types of biological variance should be considered, depending on the objectives of a particular study. For example, when using tumor and normal tissues collected from the same individual to study the signature of a cancer, anatomic variance should be considered. In clinical studies seeking to identify biomarkers for cancer classification, in which the subject of the experiment is of the same race, individual variance should be considered. When experimental subjects of clinical studies include individuals from different races, inter-population variance should be considered. Different sampling contributes different levels of variance, and such factors should be considered in the experimental design and statistical model.
Our results indicate that “noisy” genes are falsely identified as differentially expressed genes when the level of variance is underestimated, and applying a higher fold change as the selection criterion reduces/eliminates the differences between distinct estimations of variance.