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Regulation of RNA polymerase II (RNAPII)-mediated transcription controls cellular phenotypes such as cancer. Phosphatase and tensin homologue deleted on chromosome ten (PTEN), one of the most commonly altered tumor suppressors in cancer, affects transcription via its role in antagonizing the PI3K/AKT signaling pathway. Using co-immunoprecipitations and proximal ligation assays we provide evidence that PTEN interacts with AFF4, RNAPII, CDK9, cyclin T1, XPB and CDK7. Using ChIP-seq, we show that PTEN co-localizes with RNAPII and binds to chromatin in promoter and putative enhancer regions identified by histone modifications. Furthermore, we show that loss of PTEN affects RNAPII occupancy in gene bodies and further correlates with gene expression changes. Interestingly, PTEN binds to promoters and negatively regulates the expression of genes involved in transcription including AFF4 and POL2RA, which encodes a subunit of RNAPII. Loss of PTEN also increased cells’ sensitivity to transcription inhibition via small molecules, which could provide a strategy to target PTEN-deficient cancers. Overall, our work describes a previously unappreciated role of nuclear PTEN, which by interacting with the transcription machinery in the context of chromatin exerts an additional layer of regulatory control on RNAPII-mediated transcription.

The rules of engagement between zinc finger transcription factors and DNA have been partly defined by in vitro DNA-binding and structural studies, but less is known about how these rules apply in vivo. Here, we demonstrate how a missense mutation in the second zinc finger of Krüppel-like factor-1 (KLF1) leads to degenerate DNA-binding specificity in vivo, resulting in ectopic transcription and anemia in the Nan mouse model. We employed ChIP-seq and 4sU-RNA-seq to identify aberrant DNA-binding events genome wide and ectopic transcriptional consequences of this binding. We confirmed novel sequence specificity of the mutant recombinant zinc finger domain by performing biophysical measurements of in vitro DNA-binding affinity. Together, these results shed new light on the mechanisms by which missense mutations in DNA-binding domains of transcription factors can lead to autosomal dominant diseases.

We have collected over half a million splice sites from five species—Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans and Arabidopsis thaliana—and classified them into four subtypes: U2-type GT–AG and GC–AG and U12-type GT–AG and AT–AC. We have also found new examples of rare splice-site categories, such as U12-type introns without canonical borders, and U2-dependent AT–AC introns. The splice-site sequences and several tools to explore them are available on a public website (SpliceRack). For the U12-type introns, we find several features conserved across species, as well as a clustering of these introns on genes. Using the information content of the splice-site motifs, and the phylogenetic distance between them, we identify: (i) a higher degree of conservation in the exonic portion of the U2-type splice sites in more complex organisms; (ii) conservation of exonic nucleotides for U12-type splice sites; (iii) divergent evolution of C.elegans 3′ splice sites (3′ss) and (iv) distinct evolutionary histories of 5′ and 3′ss. Our study proves that the identification of broad patterns in naturally-occurring splice sites, through the analysis of genomic datasets, provides mechanistic and evolutionary insights into pre-mRNA splicing. Published version

Use of RNA interference (RNAi) in forward genetic screens is proliferating. Currently, short-interfering RNAs (siRNAs) and short-hairpin RNAs (shRNAs) are being used to silence genes to tease out functional information. It is becoming easier to harness RNAi to silence specific genes, owing to the development of libraries of readymade shRNA and siRNA gene-silencing constructs by using a variety of sources. RNAi Codex, which consists of a database of shRNA related information and an associated website, has been developed as a portal for publicly available shRNA resources and is accessible at http://codex.cshl.org. RNAi Codex currently holds data from the Hannon-Elledge shRNA library and allows the use of biologist-friendly gene names to access information on shRNA constructs that can silence the gene of interest. It is designed to hold user-contributed annotations and publications for each construct, as and when such data become available. We will describe features of RNAi Codex and explain the use of the tool.

RNAi has shown great potential for use as a tool for biological discovery, analysis and therapeutics. The involvement of the RNAi pathway in post-transcription silencing, transcriptional silencing and epigenetic silencing as well as its use as a tool for forward genetics and therapeutics throws up several bioinformatics challenges. This paper delineates several areas of research and reviews work that has already been done, the tools that are available and the challenges that lie ahead.

Cryptic splice sites are used only when use of a natural splice site is disrupted by mutation. To determine the features that distinguish authentic from cryptic 5′ splice sites (5′ss), we systematically analyzed a set of 76 cryptic 5′ss derived from 46 human genes. These cryptic 5′ss have a similar frequency distribution in exons and introns, and are usually located close to the authentic 5′ss. Statistical analysis of the strengths of the 5′ss using the Shapiro and Senapathy matrix revealed that authentic 5′ss have significantly higher score values than cryptic 5′ss, which in turn have higher values than the mutant ones. β‐Globin provides an interesting exception to this rule, so we chose it for detailed experimental analysis in vitro. We found that the sequences of the β‐globin authentic and cryptic 5′ss, but not their surrounding context, determine the correct 5′ss choice, although their respective scores do not reflect this functional difference. Our analysis provides a statistical basis to explain the competitive advantage of authentic over cryptic 5′ss in most cases, and should facilitate the development of tools to reliably predict the effect of disease‐associated 5′ss‐disrupting mutations at the mRNA level. Accepted version

MiST is a novel approach to variant calling from deep sequencing data, using the inverted mapping approach developed for Geoseq. Reads that can map to a targeted exonic region are identified using exact matches to tiles from the region. The reads are then aligned to the targets to discover variants. MiST carefully handles paralogous reads that map ambiguously to the genome and clonal reads arising from PCR bias, which are the two major sources of errors in variant calling. The reduced computational complexity of mapping selected reads to targeted regions of the genome improves speed, specificity and sensitivity of variant detection. Compared with variant calls from the GATK platform, MiST showed better concordance with SNPs from dbSNP and genotypes determined by an exonic-SNP array. Variant calls made only by MiST confirm at a high rate (>90%) by Sanger sequencing. Thus, MiST is a valuable alternative tool to analyse variants in deep sequencing data.

Deep sequencing of small RNAs (sRNA-seq) is now the gold standard for small RNA profiling and discovery. Biases in sRNA-seq have been reported, but their etiology remains unidentified. Through a comprehensive series of sRNA-seq experiments, we establish that the predominant cause of the bias is the RNA ligases. We further demonstrate that RNA ligases have strong sequence-specific biases which distort the small RNA profiles considerably. We have devised a pooled adapter strategy to overcome this bias, and validated the method through data derived from microarray and qPCR. In light of our findings, published small RNA profiles, as well as barcoding strategies using adapter-end modifications, may need to be revisited. Importantly, by providing a wide spectrum of substrate for the ligase, the pooled-adapter strategy developed here provides a means to overcome issues of bias, and generate more accurate small RNA profiles.