Search

The Long-read RNA-Seq Genome Annotation Assessment Project Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. Using different protocols and sequencing platforms, the consortium generated over 427 million long-read sequences from complementary DNA and direct RNA datasets, encompassing human, mouse and manatee species. Developers utilized these data to address challenges in transcript isoform detection, quantification and de novo transcript detection. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. Incorporating additional orthogonal data and replicate samples is advised when aiming to detect rare and novel transcripts or using reference-free approaches. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.

The rise in throughput and quality of long-read sequencing should allow unambiguous identification of full-length transcript isoforms. However, its application to single-cell RNA-seq has been limited by throughput and expense. Here we develop and characterize long-read Split-seq (LR-Split-seq), which uses combinatorial barcoding to sequence single cells with long reads. Applied to the C2C12 myogenic system, LR-split-seq associates isoforms to cell types with relative economy and design flexibility. We find widespread evidence of changing isoform expression during differentiation including alternative transcription start sites (TSS) and/or alternative internal exon usage. LR-Split-seq provides an affordable method for identifying cluster-specific isoforms in single cells.

During mammalian embryogenesis, differential gene expression gradually builds the identity and complexity of each tissue and organ system1. Here we systematically quantified mouse polyA-RNA from day 10.5 of embryonic development to birth, sampling 17 tissues and organs. The resulting developmental transcriptome is globally structured by dynamic cytodifferentiation, body-axis and cell-proliferation gene sets that were further characterized by the transcription factor motif codes of their promoters. We decomposed the tissue-level transcriptome using single-cell RNA-seq (sequencing of RNA reverse transcribed into cDNA) and found that neurogenesis and haematopoiesis dominate at both the gene and cellular levels, jointly accounting for one-third of differential gene expression and more than 40% of identified cell types. By integrating promoter sequence motifs with companion ENCODE epigenomic profiles, we identified a prominent promoter de-repression mechanism in neuronal expression clusters that was attributable to known and novel repressors. Focusing on the developing limb, single-cell RNA data identified 25 candidate cell types that included progenitor and differentiating states with computationally inferred lineage relationships. We extracted cell-type transcription factor networks and complementary sets of candidate enhancer elements by using single-cell RNA-seq to decompose integrative cis-element (IDEAS) models that were derived from whole-tissue epigenome chromatin data. These ENCODE reference data, computed network components and IDEAS chromatin segmentations are companion resources to the matching epigenomic developmental matrix, and are available for researchers to further mine and integrate.

Cellular reprogramming highlights the epigenetic plasticity of the somatic cell state. Long noncoding RNAs (lncRNAs) have emerging roles in epigenetic regulation, but their potential functions in reprogramming cell fate have been largely unexplored. We used single-cell RNA sequencing to characterize the expression patterns of over 16,000 genes, including 437 lncRNAs, during defined stages of reprogramming to pluripotency. Self-organizing maps (SOMs) were used as an intuitive way to structure and interrogate transcriptome data at the single-cell level. Early molecular events during reprogramming involved the activation of Ras signaling pathways, along with hundreds of lncRNAs. Loss-of-function studies showed that activated lncRNAs can repress lineage-specific genes, while lncRNAs activated in multiple reprogramming cell types can regulate metabolic gene expression. Our findings demonstrate that reprogramming cells activate defined sets of functionally relevant lncRNAs and provide a resource to further investigate how dynamic changes in the transcriptome reprogram cell state.

We tested whether self-organizing maps (SOMs) could be used to effectively integrate, visualize, and mine diverse genomics data types, including complex chromatin signatures. A fine-grained SOM was trained on 72 ChIP-seq histone modifications and DNase-seq data sets from six biologically diverse cell lines studied by The ENCODE Project Consortium. We mined the resulting SOM to identify chromatin signatures related to sequence-specific transcription factor occupancy, sequence motif enrichment, and biological functions. To highlight clusters enriched for specific functions such as transcriptional promoters or enhancers, we overlaid onto the map additional data sets not used during training, such as ChIP-seq, RNA-seq, CAGE, and information on cis-acting regulatory modules from the literature. We used the SOM to parse known transcriptional enhancers according to the cell-type-specific chromatin signature, and we further corroborated this pattern on the map by EP300 (also known as p300) occupancy. New candidate cell-type-specific enhancers were identified for multiple ENCODE cell types in this way, along with new candidates for ubiquitous enhancer activity. An interactive web interface was developed to allow users to visualize and custom-mine the ENCODE SOM. We conclude that large SOMs trained on chromatin data from multiple cell types provide a powerful way to identify complex relationships in genomic data at user-selected levels of granularity.

A current challenge is to develop computational approaches to infer gene network regulatory relationships based on multiple types of large-scale functional genomic data. We find that single-layer feed-forward artificial neural network (ANN) models can effectively discover gene network structure by integrating global in vivo protein: DNA interaction data (ChIP/ Array) with genome-wide microarray RNA data. We test this on the yeast cell cycle transcription network, which is composed of several hundred genes with phase-specific RNA outputs. These ANNs were robust to noise in data and to a variety of perturbations. They reliably identified and ranked 10 of 12 known major cell cycle factors at the top of a set of 204, based on a sum-of-squared weights metric. Comparative analysis of motif occurrences among multiple yeast species independently confirmed relationships inferred from ANN weights analysis. ANN models can capitalize on properties of biological gene networks that other kinds of models do not. ANNs naturally take advantage of patterns of absence, as well as presence, of factor binding associated with specific expression output; they are easily subjected to in silico "mutation'' to uncover biological redundancies; and they can use the full range of factor binding values. A prominent feature of cell cycle ANNs suggested an analogous property might exist in the biological network. This postulated that "network- local discrimination'' occurs when regulatory connections (here between MBF and target genes) are explicitly disfavored in one network module (G2), relative to others and to the class of genes outside the mitotic network. If correct, this predicts that MBF motifs will be significantly depleted from the discriminated class and that the discrimination will persist through evolution. Analysis of distantly related Schizosaccharomyces pombe confirmed this, suggesting that network-local discrimination is real and complements well- known enrichment of MBF sites in G1 class genes.

Analysis of large-scale gene expression studies usually begins with gene clustering. A ubiquitous problem is that different algorithms applied to the same data inevitably give different results, and the differences are often substantial, involving a quarter or more of the genes analyzed. This raises a series of important but nettlesome questions: How are different clustering results related to each other and to the underlying data structure? Is one clustering objectively superior to another? Which differences, if any, are likely candidates to be biologically important? A systematic and quantitative way to address these questions is needed, together with an effective way to integrate and leverage expression results with other kinds of large-scale data and annotations. We developed a mathematical and computational framework to help quantify, compare, visualize and interactively mine clusterings. We show that by coupling confusion matrices with appropriate metrics (linear assignment and normalized mutual information scores), one can quantify and map differences between clusterings. A version of receiver operator characteristic analysis proved effective for quantifying and visualizing cluster quality and overlap. These methods, plus a flexible library of clustering algorithms, can be called from a new expandable set of software tools called CompClust 1.0 (). CompClust also makes it possible to relate expression clustering patterns to DNA sequence motif occurrences, protein–DNA interaction measurements and various kinds of functional annotations. Test analyses used yeast cell cycle data and revealed data structure not obvious under all algorithms. These results were then integrated with transcription motif and global protein–DNA interaction data to identify G1 regulatory modules.

A report that Shewanella oneidensis biofilm structures reproductivity stratify with respect to growth activity and metabolism as a function of size is presented. Findings reveal that, far from being dead, the majority of cells in mature S. oneidensis biofilms have actively turned-on metabolic programs appropriate to their local microenvironment and development stage.

The investigation and modeling of gene regulatory networks requires computational tools specific to the task. We present several locally developed software tools that have been used in support of our ongoing research into the embryogenesis of the sea urchin. These tools are especially well suited to iterative refinement of models through experimental and computational investigation. They include: BioArray, a macroarray spot processing program; SUGAR, a system to display and correlate large-BAC sequence analyses; SeqComp and FamilyRelations, programs for comparative sequence analysis; and NetBuilder, an environment for creating and analyzing models of gene networks. We also present an overview of the process used to build our model of the Strongylocentrotus purpuratus endomesoderm gene network. Several of the tools discussed in this paper are still in active development and some are available as open source. Key Words: sequence annotation; comparative analysis; macroarrays; gene network modeling; computational tools.

Three different null alleles of the myogenic bHLH gene MRF4/herculin/Myf-6 were created recently. The three alleles were similar in design but were surprisingly different in the intensity of their phenotypes, which ranged from complete viability of homozygotes to complete lethality. One possible explanation for these differences is that each mutation altered expression from the nearby Myf-5 gene to a different extent. This possibility was first raised by the observation that the most severe MRF4 knockout allele expresses no Myf-5 RNA and is a developmental phenocopy of the Myf-5 null mutation. Furthermore, initial studies of the two weaker alleles had shown that their differences in viability correlate with the intensity of rib skeletal defects, and the most extreme version of this rib defect is the hallmark phenotype of Myf-5 null animals. In the present study we tested this hypothesis for the two milder MRF4 alleles. By analyzing compound heterozygous animals carrying either the intermediate or the weakest MRF4 knockout allele on one chromosome 10 and a Myf-5 knockout allele on the other chromosome, we found that both of these MRF4 alleles apparently downregulate Myf-5 expression by a cis- acting mechanism. Compound heterozygotes Showed increased mortality of the normally viable MRF4 allele, together with intensified rib defects for both MRF4 alleles and increased deficits in myotomal Myf-5 expression. The allele-specific gradation in phenotypes also suggested that rib morphogenesis is profoundly sensitive to quantitative differences in Myf- 5 function if Myf-5 products drop below hemizygous levels. The mechanistic basis for cis interactions at the MRF4/Myf- 5 locus was further examined by fusing a DNA segment containing the entire MRF4 structural gene, including all sequences deleted in the three MRF knockout alleles, with a basal promoter and a lacZ reporter. Transgenic embryos showed specific LacZ expression in myotomes in a pattern that closely resembles the expression of Myf-5 RNA. cis-acting interactions between Myf-5 and MRF4 may therefore play a significant role in regulating expression of these genes in the early myotomes of wildtype embryos.

A study examined the extent of inhibition on the progress of primer extension of DNA polymerase by oligonucleotide directed triple helices. The progress of the Klenow fragment present between duplex and triplex near the proximal boundary is restricted by the major groove of a DNA substrate of the double helical type, whose functional time duration was determined by kinetic analysis. The results obtained form the fundamental principles of DNA polymerase repair by defined local three stranded analysis DNA probes.

Abstract Background Hundreds of genes, including muscle creatine kinase (MCK), are differentially expressed in fast- and slow-twitch muscle fibers, but the fiber type-specific regulatory mechanisms are not well understood. Results Modulatory region 1 (MR1) is a 1-kb regulatory region within MCK intron 1 that is highly active in terminally differentiating skeletal myocytes in vitro. A MCK small intronic enhancer (MCK-SIE) containing a paired E-box/myocyte enhancer factor 2 (MEF2) regulatory motif resides within MR1. The SIE's transcriptional activity equals that of the extensively characterized 206-bp MCK 5'-enhancer, but the MCK-SIE is flanked by regions that can repress its activity via the individual and combined effects of about 15 different but highly conserved 9- to 24-bp sequences. ChIP and ChIP-Seq analyses indicate that the SIE and the MCK 5'-enhancer are occupied by MyoD, myogenin and MEF2. Many other E-boxes located within or immediately adjacent to intron 1 are not occupied by MyoD or myogenin. Transgenic analysis of a 6.5-kb MCK genomic fragment containing the 5'-enhancer and proximal promoter plus the 3.2-kb intron 1, with and without MR1, indicates that MR1 is critical for MCK expression in slow- and intermediate-twitch muscle fibers (types I and IIa, respectively), but is not required for expression in fast-twitch muscle fibers (types IIb and IId). Conclusions In this study, we discovered that MR1 is critical for MCK expression in slow- and intermediate-twitch muscle fibers and that MR1's positive transcriptional activity depends on a paired E-box MEF2 site motif within a SIE. This is the first study to delineate the DNA controls for MCK expression in different skeletal muscle fiber types.