Recent advances in high-throughput cDNA sequencing (RNA-seq) can reveal new genes and splice variants and quantify expression genome-wide in a single assay. The volume and complexity of data from RNA-seq experiments necessitate scalable, fast and mathematically principled analysis software. TopHat and Cufflinks are free, open-source software tools for gene discovery and comprehensive expression analysis of high-throughput mRNA sequencing (RNA-seq) data. Together, they allow biologists to identify new genes and new splice variants of known ones, as well as compare gene and transcript expression under two or more conditions. This protocol describes in detail how to use TopHat and Cufflinks to perform such analyses. It also covers several accessory tools and utilities that aid in managing data, including CummeRbund, a tool for visualizing RNA-seq analysis results. Although the procedure assumes basic informatics skills, these tools assume little to no background with RNA-seq analysis and are meant for novices and experts alike. The protocol begins with raw sequencing reads and produces a transcriptome assembly, lists of differentially expressed and regulated genes and transcripts, and publication-quality visualizations of analysis results. The protocol's execution time depends on the volume of transcriptome sequencing data and available computing resources but takes less than 1 d of computer time for typical experiments and ~1 h of hands-on time.

Background: The genome of the domestic cow, Bos taurus, was sequenced using a mixture of hierarchical and whole-genome shotgun sequencing methods.

The complex language of eukaryotic gene expression remains incompletely understood. Despite the importance suggested by many noncoding variants statistically associated with human disease, nearly all such variants have unknown mechanisms. Here, we address this challenge using an approach based on a recent machine learning advance—deep convolutional neural networks (CNNs). We introduce the open source package Basset to apply CNNs to learn the functional activity of DNA sequences from genomics data. We trained Basset on a compendium of accessible genomic sites mapped in 164 cell types by DNase-seq, and demonstrate greater predictive accuracy than previous methods. Basset predictions for the change in accessibility between variant alleles were far greater for Genome-wide association study (GWAS) SNPs that are likely to be causal relative to nearby SNPs in linkage disequilibrium with them. With Basset, a researcher can perform a single sequencing assay in their cell type of interest and simultaneously learn that cell's chromatin accessibility code and annotate every mutation in the genome with its influence on present accessibility and latent potential for accessibility. Thus, Basset offers a powerful computational approach to annotate and interpret the noncoding genome.

RNA is known to be an abundant and important structural component of the nuclear matrix, including long noncoding RNAs (lncRNA). Yet the molecular identities, functional roles, and localization dynamics of lncRNAs that influence nuclear architecture remain poorly understood. Here, we describe one lncRNA, Firre, that interacts with the nuclear matrix factor hnRNPU, through a 156 bp repeating sequence and Firre localizes across a ~5 Mb domain on the X-chromosome. We further observed Firre localization across at least five distinct trans-chromosomal loci, which reside in spatial proximity to the Firre genomic locus on the X-chromosome. Both genetic deletion of the Firre locus or knockdown of hnRNPU resulted in loss of co-localization of these trans-chromosomal interacting loci. Thus, our data suggest a model in which lncRNAs such as Firre can interface with and modulate nuclear architecture across chromosomes.

BackgroundNumerous studies over the past decade have elucidated a large set of long intergenic noncoding RNAs (lincRNAs) in the human genome. Research since has shown that lincRNAs constitute an important layer of genome regulation across a wide spectrum of species. However, the factors governing their evolution and origins remain relatively unexplored. One possible factor driving lincRNA evolution and biological function is transposable element (TE) insertions. Here, we comprehensively characterize the TE content of lincRNAs relative to genomic averages and protein coding transcripts.ResultsOur analysis of the TE composition of 9,241 human lincRNAs revealed that, in sharp contrast to protein coding genes, 83% of lincRNAs contain a TE, and TEs comprise 42% of lincRNA sequence. lincRNA TE composition varies significantly from genomic averages - L1 and Alu elements are depleted and broad classes of endogenous retroviruses are enriched. TEs occur in biased positions and orientations within lincRNAs, particularly at their transcription start sites, suggesting a role in lincRNA transcriptional regulation. Accordingly, we observed a dramatic example of HERVH transcriptional regulatory signals correlating strongly with stem cell-specific expression of lincRNAs. Conversely, lincRNAs devoid of TEs are expressed at greater levels than lincRNAs with TEs in all tissues and cell lines, particularly in the testis.ConclusionsTEs pervade lincRNAs, dividing them into classes, and may have shaped lincRNA evolution and function by conferring tissue-specific expression from extant transcriptional regulatory signals.

We introduce Quake, a program to detect and correct errors in DNA sequencing reads. Using a maximum likelihood approach incorporating quality values and nucleotide specific miscall rates, Quake achieves the highest accuracy on realistically simulated reads. We further demonstrate substantial improvements in de novo assembly and SNP detection after using Quake. Quake can be used for any size project, including more than one billion human reads, and is freely available as open source software from http://www.cbcb.umd.edu/software/quake.

Differentiation of human embryonic stem cells (hESCs) provides a unique opportunity to study the regulatory mechanisms that facilitate cellular transitions in a human context. To that end, we performed comprehensive transcriptional and epigenetic profiling of populations derived through directed differentiation of hESCs representing each of the three embryonic germ layers. Integration of whole genome bisulfite sequencing, chromatin immunoprecipitation-sequencing and RNA-sequencing reveals unique events associated with specification towards each lineage. Dynamic alterations in DNA methylation and H3K4me1 are evident at putative distal regulatory elements bound by pluripotency factors or activated in specific lineages. In addition, we identified germ layer-specific H3K27me3 enrichment at sites exhibiting high DNA methylation in the undifferentiated state. A better understanding of these initial specification events will facilitate identification of deficiencies in current approaches leading to more faithful differentiation strategies as well as provide insights into the rewiring of human regulatory programs during cellular transitions.

Low-cost short read sequencing technology has revolutionized genomics, though it is only just becoming practical for the high-quality de novo assembly of a novel large genome. We describe the Assemblathon 1 competition, which aimed to comprehensively assess the state of the art in de novo assembly methods when applied to current sequencing technologies. In a collaborative effort, teams were asked to assemble a simulated Illumina HiSeq data set of an unknown, simulated diploid genome. A total of 41 assemblies from 17 different groups were received. Novel haplotype aware assessments of coverage, contiguity, structure, base calling, and copy number were made. We establish that within this benchmark: (1) It is possible to assemble the genome to a high level of coverage and accuracy, and that (2) large differences exist between the assemblies, suggesting room for further improvements in current methods. The simulated benchmark, including the correct answer, the assemblies, and the code that was used to evaluate the assemblies is now public and freely available from http://www.assemblathon.org/.