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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
02/12/2010 |
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Data da última atualização: |
15/01/2020 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
REIS, O.; COSTA, G. G. L.; HERAI, R. H.; CARAZZOLLE, M. F.; PEREIRA, G. A. G. |
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Afiliação: |
LGE/UNICAMP; LGE/UNICAMP; LGE/UNICAMP, LBA/CNPTIA; LGE, CENAPAD/UNICAMP; LGE/UNICAMP. |
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Título: |
RNA-seq: the need for biological replicates. |
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Ano de publicação: |
2010 |
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Fonte/Imprenta: |
In: INTERNATIONAL CONFERENCE OF THE BRAZILIAN ASSOCIATION FOR BIOINFORMATICS AND COMPUTATIONAL BIOLOGY, 6., 2010, Ouro Preto. Abstracts... [S.l.: s.n.], 2010. |
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Páginas: |
p. 194. |
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Idioma: |
Inglês |
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Notas: |
AB3C X-meeting 2010. |
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Conteúdo: |
RNA-seq provides a way to analyze entire transcriptomes with deep coverage and base level resolution and it is gradually replacing microarrays for gene expression analyses. In the past few years, many statistical methods have been developed to improve the detection of differentially expressed genes from RNA-seq. However the replacing of microarrays by RNA-seq has been often accompanied by decline in experimental design quality, as many groups working with RNA-seq are not using biological replicates. For any statistical inference it is necessary replication, for example, if you ?nd a couple of genes that are upregulated in the treatment in comparison to the value in control, without replication you can not know if that is effect of random variability or an actual effect of the treatment. Furthermore, without biological replicates it is too dif?cult to estimate the sample variation. The authors of EdgeR and DESeq propose a similar method to work without replicates. They propose an approach to infer an upper limit for the sample variance that uses both treatment and control as they were biological replicates. If it is expected that the most genes are not differently regulated, the calculated sample variance should be close to real sample variance. In this work, we evaluate the effect of working with and without biological replicates on the list of differentially expressed genes obtained. We show that the use of proper biological replicates is important to get good sensitivity to detect differentially expressed genes, showing that the replacement of microarrays by RNA-seq does not justify the use of an inferior experimental design. MenosRNA-seq provides a way to analyze entire transcriptomes with deep coverage and base level resolution and it is gradually replacing microarrays for gene expression analyses. In the past few years, many statistical methods have been developed to improve the detection of differentially expressed genes from RNA-seq. However the replacing of microarrays by RNA-seq has been often accompanied by decline in experimental design quality, as many groups working with RNA-seq are not using biological replicates. For any statistical inference it is necessary replication, for example, if you ?nd a couple of genes that are upregulated in the treatment in comparison to the value in control, without replication you can not know if that is effect of random variability or an actual effect of the treatment. Furthermore, without biological replicates it is too dif?cult to estimate the sample variation. The authors of EdgeR and DESeq propose a similar method to work without replicates. They propose an approach to infer an upper limit for the sample variance that uses both treatment and control as they were biological replicates. If it is expected that the most genes are not differently regulated, the calculated sample variance should be close to real sample variance. In this work, we evaluate the effect of working with and without biological replicates on the list of differentially expressed genes obtained. We show that the use of proper biological replicates is important to get good sensitivity t... Mostrar Tudo |
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Palavras-Chave: |
Repetições biológicas; RNA sequences; Sequências de RNA. |
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Thesaurus NAL: |
Sequence analysis. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02414nam a2200229 a 4500
001 1868535
005 2020-01-15
008 2010 bl uuuu u00u1 u #d
100 1 $aREIS, O.
245 $aRNA-seq$bthe need for biological replicates.$h[electronic resource]
260 $aIn: INTERNATIONAL CONFERENCE OF THE BRAZILIAN ASSOCIATION FOR BIOINFORMATICS AND COMPUTATIONAL BIOLOGY, 6., 2010, Ouro Preto. Abstracts... [S.l.: s.n.], 2010.$c2010
300 $ap. 194.
500 $aAB3C X-meeting 2010.
520 $aRNA-seq provides a way to analyze entire transcriptomes with deep coverage and base level resolution and it is gradually replacing microarrays for gene expression analyses. In the past few years, many statistical methods have been developed to improve the detection of differentially expressed genes from RNA-seq. However the replacing of microarrays by RNA-seq has been often accompanied by decline in experimental design quality, as many groups working with RNA-seq are not using biological replicates. For any statistical inference it is necessary replication, for example, if you ?nd a couple of genes that are upregulated in the treatment in comparison to the value in control, without replication you can not know if that is effect of random variability or an actual effect of the treatment. Furthermore, without biological replicates it is too dif?cult to estimate the sample variation. The authors of EdgeR and DESeq propose a similar method to work without replicates. They propose an approach to infer an upper limit for the sample variance that uses both treatment and control as they were biological replicates. If it is expected that the most genes are not differently regulated, the calculated sample variance should be close to real sample variance. In this work, we evaluate the effect of working with and without biological replicates on the list of differentially expressed genes obtained. We show that the use of proper biological replicates is important to get good sensitivity to detect differentially expressed genes, showing that the replacement of microarrays by RNA-seq does not justify the use of an inferior experimental design.
650 $aSequence analysis
653 $aRepetições biológicas
653 $aRNA sequences
653 $aSequências de RNA
700 1 $aCOSTA, G. G. L.
700 1 $aHERAI, R. H.
700 1 $aCARAZZOLLE, M. F.
700 1 $aPEREIRA, G. A. G.
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