Genome-wide mapping of the distribution of CarD, RNAP σA, and RNAP β on the Mycobacterium smegmatis chromosome using chromatin immunoprecipitation sequencing

CarD is an essential mycobacterial protein that binds the RNA polymerase (RNAP) and affects the transcriptional profile of Mycobacterium smegmatis and Mycobacterium tuberculosis [6]. We predicted that CarD was directly regulating RNAP function but our prior experiments had not determined at what stage of transcription CarD was functioning and at which genes CarD interacted with the RNAP. To begin to address these open questions, we performed chromatin immunoprecipitation sequencing (ChIP-seq) to survey the distribution of CarD throughout the M. smegmatis chromosome. The distribution of RNAP subunits β and σA were also profiled. We expected that RNAP β would be present throughout transcribed regions and RNAP σA would be predominantly enriched at promoters based on work in Escherichia coli [3], however this had yet to be determined in mycobacteria. The ChIP-seq analyses revealed that CarD was never present on the genome in the absence of RNAP, was primarily associated with promoter regions, and was highly correlated with the distribution of RNAP σA. The colocalization of σA and CarD led us to propose that in vivo, CarD associates with RNAP initiation complexes at most promoters and is therefore a global regulator of transcription initiation. Here we describe in detail the data from the ChIP-seq experiments associated with the study published by Srivastava and colleagues in the Proceedings of the National Academy of Science in 2013 [5] as well as discuss the findings from this dataset in relation to both CarD and mycobacterial transcription as a whole. The ChIP-seq data have been deposited in the Gene Expression Omnibus (GEO) database, www.ncbi.nlm.nih.gov/geo (accession no. GSE48164)

Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data

A large number of computational methods have been developed for analyzing differential gene expression in RNA-seq data. We describe a comprehensive evaluation of common methods using the SEQC benchmark dataset and ENCODE data. We consider a number of key features, including normalization, accuracy of differential expression detection and differential expression analysis when one condition has no detectable expression. We find significant differences among the methods, but note that array-based methods adapted to RNA-seq data perform comparably to methods designed for RNA-seq. Our results demonstrate that increasing the number of replicate samples significantly improves detection power over increased sequencing depth

A Genome-Wide Map of Conserved MicroRNA Targets in C. elegans

SummaryBackgroundMetazoan miRNAs regulate protein-coding genes by binding the 3′ UTR of cognate mRNAs. Identifying targets for the 115 known C. elegans miRNAs is essential for understanding their function.ResultsBy using a new version of PicTar and sequence alignments of three nematodes, we predict that miRNAs regulate at least 10% of C. elegans genes through conserved interactions. We have developed a new experimental pipeline to assay 3′ UTR-mediated posttranscriptional gene regulation via an endogenous reporter expression system amenable to high-throughput cloning, demonstrating the utility of this system using one of the most intensely studied miRNAs, let-7. Our expression analyses uncover several new potential let-7 targets and suggest a new let-7 activity in head muscle and neurons. To explore genome-wide trends in miRNA function, we analyzed functional categories of predicted target genes, finding that one-third of C. elegans miRNAs target gene sets are enriched for specific functional annotations. We have also integrated miRNA target predictions with other functional genomic data from C. elegans.ConclusionsAt least 10% of C. elegans genes are predicted miRNA targets, and a number of nematode miRNAs seem to regulate biological processes by targeting functionally related genes. We have also developed and successfully utilized an in vivo system for testing miRNA target predictions in likely endogenous expression domains. The thousands of genome-wide miRNA target predictions for nematodes, humans, and flies are available from the PicTar website and are linked to an accessible graphical network-browsing tool allowing exploration of miRNA target predictions in the context of various functional genomic data resources

Identification of microRNA targets

microRNAs (miRNAs) are a recently discovered class of small non-coding RNAs. Their mature form is 20-25 nucleotides (nt) long, and is derived from a longer hairpin precursor. Almost 700 miRNAs are currently known in human. miRNAs regulate their target genes by blocking their translation into protein, or by compromising the stability of a messenger RNA (mRNA), leading to its degradation. To understand the biological function of microRNAs it is crucial to identify their target genes. miRNAs and mRNAs interact through partial Watson-Crick complementarity, and it is known that binding sites of conserved miRNAs are often conserved themselves. It is believed that several miRNAs can regulate the same gene simultaneously. Taking into account the characteristics of miRNA::mRNA interaction, we designed a probabilistic sequence model that enables us to estimate the likelihood for each gene in the data set to be a target of a given set of miRNAs. By requiring the presence of some number of binding sites conserved in related species, we are applying a filter that keeps the false positive predictions number at an acceptable level. We used the method to predict targets of all human, worm and fruit fly miRNAs, and many predictions were later confirmed by experiments. Perhaps surprisingly, our data predicted that a single miRNA regulated the expression of hundreds of mRNAs and that altogether thousands of human genes are regulated by miRNAs. This prediction has by now been widely accepted. Among other predictions, we found that a mouse gene Myothrophin is predicted to be coordinately targeted by three miRNAs, and this was experimentally confirmed in cell lines. In order to discover the sequence motifs that are mediating the effect of miRNAs on their targets, we analyzed the genome-wide expression data obtained by miRNAs. Using an iterative linear regression model, we could select the motifs that best explain the genome-wide changes in expression imposed by the activity of miRNAs. In the end, we apply both methods to analyze experiments performed on B cells (immune system), and were able to in part uncover the role of miRNAs in B cell development