RNA-Seq Identifies Gene Expression Changes Associated with Relapse in Acute Lymphoblastic Leukemia (ALL)
In this study a team of researchers from Life Technologies and St. Jude Children's Research Hospital have performed RNA-Seq using the SOLiD™3 system on samples from five children with acute lymphoblastic leukemia (ALL) with paired samples taken at the time of initial presentation with cancer (“initial” or “I”) and at the time of relapse (“relapse” or “R”).Raw data was processed using the analysis pipelines in Bioscope and tertiary analysis was performed using several published statistical methods. These methods identified gene sets that can select cancer relapse samples with almost 98% accuracy, and have further identified genes that were found to be correlated with tumor relapse and poor prognosis in other studies. The analysis have also identified nucleotide variants unique to the samples at the time of relapse. The researchers further note that processing of the data using a Bayesian method such as Bayseq appears to alleviate sample and run specific ...
How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?
RNA-seq is now the technology of choice for genome-wide differential gene expression experiments, but it is not clear how many biological replicates are needed to ensure valid biological interpretation of the results or which statistical tools are best for analyzing the data. Researcher from the University of Dundee performed an RNA-seq experiment with 48 biological replicates in each of two conditions to answer these questions and provide guidelines for experimental design. With three biological replicates, eight of the 11 tools evaluated found only 20%-40% of the significantly differentially expressed (SDE) genes identified with the full set of 42 clean replicates. This rises to >85% for the subset of SDE genes changing in expression by more than fourfold. To achieve >85% for all SDE genes regardless of fold change requires more than 20 biological replicates. The same eight tools successfully control their false discovery rate at ≲5% for all numbers of ...
A Systematic Evaluation of Feature Selection and Classification Algorithms Using Simulated and Real miRNA Sequencing Data
Sequencing is widely used to discover associations between microRNAs (miRNAs) and diseases. However, the negative binomial distribution (NB) and high dimensionality of data obtained using sequencing can lead to low-power results and low reproducibility. Several statistical learning algorithms have been proposed to address sequencing data, and although evaluation of these methods is essential, such studies are relatively rare. Researchers from Nanjing Medical University, compared the performance of seven feature selection (FS) algorithms, including: baySeq DESeq edgeR the rank sum test lasso particle swarm optimistic decision tree and random forest (RF) The algorithms were compared by simulation under different conditions based on the difference of the mean, the dispersion parameter of the NB, and the signal to noise ratio. Real data were used to evaluate the performance of RF, logistic regression, and support vector machine. Based on the simulation and real data, the researchers discuss the behaviour of the FS and ...
RNA-Seq reveals the mechanisms of transcription regulation during heart development
Dysregulation of transcription is associated with the pathogenesis of cardiovascular diseases, including congenital heart diseases and heart failure. However, it remains unclear how transcription factors regulate transcription in the heart and which genes are associated with cardiovascular diseases in humans. Development of genome-wide analyses using next-generation sequencers provides powerful methods to determine how these transcription factors and chromatin regulators control gene expressions and to identify causative genes in cardiovascular diseases. These technologies have revealed that transcription during heart development is elaborately regulated by multiple cardiac transcription factors. As the human genome size is 3G bp, next-generation sequencers can easily cover the whole human genome, thus this sequencer is suitable to use for genome-wide analysis. Indeed, many methods are developed using next-generation sequencers, such as the combination with chromatin immunoprecipitation and sequencing (ChIPseq) and whole-transcriptome shotgun sequencing (RNA sequencing; RNAseq). These technologies allow us to identify genomic mutation and to analyze ...
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