GeneProf data: a resource of curated, integrated and reusable high-throughput genomics experiments

Productive visualization of high-throughput sequencing data using the SeqCode open portable platform

Large-scale sequencing techniques to chart genomes are entirely consolidated. Stable computational methods to perform primary tasks such as quality control, read mapping, peak calling, and counting are likewise available. However, there is a lack of uniform standards for graphical data mining, which is also of central importance. To fill this gap, we developed SeqCode, an open suite of applications that analyzes sequencing data in an elegant but efficient manner. Our software is a portable resource written in ANSI C that can be expected to work for almost all genomes in any computational configuration. Furthermore, we offer a user-friendly front-end web server that integrates SeqCode functions with other graphical analysis tools. Our analysis and visualization toolkit represents a significant improvement in terms of performance and usability as compare to other existing programs. Thus, SeqCode has the potential to become a key multipurpose instrument for high-throughput professional analysis; further, it provides an extremely useful open educational platform for the world-wide scientific community. SeqCode website is hosted at http://ldicrocelab.crg.eu, and the source code is freely distributed at https://github.com/eblancoga/seqcode.

Going low to reach high: Small-scale ChIP-seq maps new terrain

Chromatin immunoprecipitation (ChIP) enables mapping of specific histone modifications or chromatin-associated factors in the genome and represents a powerful tool in the study of chromatin and genome regulation. Importantly, recent technological advances that couple ChIP with whole-genome high-throughput sequencing (ChIP-seq) now allow the mapping of chromatin factors throughout the genome. However, the requirement for large amounts of ChIP-seq input material has long made it challenging to assess chromatin profiles of cell types only available in limited numbers. For many cell types, it is not feasible to reach high numbers when collecting them as homogeneous cell populations in vivo. Nonetheless, it is an advantage to work with pure cell populations to reach robust biological conclusions. Here, we review (a) how ChIP protocols have been scaled down for use with as little as a few hundred cells; (b) which considerations to be aware of when preparing small-scale ChIP-seq and analyzing data; and (c) the potential of small-scale ChIP-seq datasets for elucidating chromatin dynamics in various biological systems, including some examples such as oocyte maturation and preimplantation embryo development. This article is categorized under: Laboratory Methods and Technologies > Genetic/Genomic Methods Developmental Biology > Developmental Processes in Health and Disease Biological Mechanisms > Cell Fates.

MGA repository: a curated data resource for ChIP-seq and other genome annotated data

The Mass Genome Annotation (MGA) repository is a resource designed to store published next generation sequencing data and other genome annotation data (such as gene start sites, SNPs, etc.) in a completely standardised format. Each sample has undergone local processing in order the meet the strict MGA format requirements. The original data source, the reformatting procedure and the biological characteristics of the samples are described in an accompanying documentation file manually edited by data curators. 10 model organisms are currently represented: Homo sapiens, Mus musculus, Danio rerio, Drosophila melanogaster, Apis mellifera, Caenorhabditis elegans, Arabidopsis thaliana, Zea mays, Saccharomyces cerevisiae and Schizosaccharomyces pombe. As of today, the resource contains over 24 000 samples. In conjunction with other tools developed by our group (the ChIP-Seq and SSA servers), it allows users to carry out a great variety of analysis task with MGA samples, such as making aggregation plots and heat maps for selected genomic regions, finding peak regions, generating custom tracks for visualizing genomic features in a UCSC genome browser window, or downloading chromatin data in a table format suitable for local processing with more advanced statistical analysis software such as R. Home page: http://ccg.vital-it.ch/mga/.

Crunch: integrated processing and modeling of ChIP-seq data in terms of regulatory motifs

Although ChIP-seq has become a routine experimental approach for quantitatively characterizing the genome-wide binding of transcription factors (TFs), computational analysis procedures remain far from standardized, making it difficult to compare ChIP-seq results across experiments. In addition, although genome-wide binding patterns must ultimately be determined by local constellations of DNA-binding sites, current analysis is typically limited to identifying enriched motifs in ChIP-seq peaks. Here we present Crunch, a completely automated computational method that performs all ChIP-seq analysis from quality control through read mapping and peak detecting and that integrates comprehensive modeling of the ChIP signal in terms of known and novel binding motifs, quantifying the contribution of each motif and annotating which combinations of motifs explain each binding peak. By applying Crunch to 128 data sets from the ENCODE Project, we show that Crunch outperforms current peak finders and find that TFs naturally separate into "solitary TFs," for which a single motif explains the ChIP-peaks, and "cobinding TFs," for which multiple motifs co-occur within peaks. Moreover, for most data sets, the motifs that Crunch identified de novo outperform known motifs, and both the set of cobinding motifs and the top motif of solitary TFs are consistent across experiments and cell lines. Crunch is implemented as a web server, enabling standardized analysis of any collection of ChIP-seq data sets by simply uploading raw sequencing data. Results are provided both in a graphical web interface and as downloadable files.

hppRNA-a Snakemake-based handy parameter-free pipeline for RNA-Seq analysis of numerous samples

RNA-Seq technology has been gradually becoming a routine approach for characterizing the properties of transcriptome in terms of organisms, cell types and conditions and consequently a big burden has been put on the facet of data analysis, which calls for an easy-to-learn workflow to cope with the increased demands from a large number of laboratories across the world. We report a one-in-all solution called hppRNA, composed of four scenarios such as pre-mapping, core-workflow, post-mapping and sequence variation detection, written by a series of individual Perl and R scripts, counting on well-established and preinstalled software, irrespective of single-end or paired-end, unstranded or stranded sequencing method. It features six independent core-workflows comprising the state-of-the-art technology with dozens of popular cutting-edge tools such as Tophat-Cufflink-Cuffdiff, Subread-featureCounts-DESeq2, STAR-RSEM-EBSeq, Bowtie-eXpress-edgeR, kallisto-sleuth, HISAT-StringTie-Ballgown, and embeds itself in Snakemake, which is a modern pipeline management system. The core function of this pipeline is turning the raw fastq files into gene/isoform expression matrix and differentially expressed genes or isoforms as well as the identification of fusion genes, single nucleotide polymorphisms, long noncoding RNAs and circular RNAs. Last but not least, this pipeline is specifically designed for performing the systematic analysis on a huge set of samples in one go, ideally for the researchers who intend to deploy the pipeline on their local servers. The scripts as well as the user manual are freely available at https://sourceforge.net/projects/hpprna/.

Esrrb extinction triggers dismantling of naïve pluripotency and marks commitment to differentiation

Self-renewal of embryonic stem cells (ESCs) cultured in LIF/fetal calf serum (FCS) is incomplete with some cells initiating differentiation. While this is reflected in heterogeneous expression of naive pluripotency transcription factors (TFs), the link between TF heterogeneity and differentiation is not fully understood. Here, we purify ESCs with distinct TF expression levels from LIF/FCS cultures to uncover early events during commitment from naïve pluripotency. ESCs carrying fluorescent Nanog and Esrrb reporters show Esrrb downregulation only in Nanoglow cells. Independent Esrrb reporter lines demonstrate that Esrrbnegative ESCs cannot effectively self-renew. Upon Esrrb loss, pre-implantation pluripotency gene expression collapses. ChIP-Seq identifies different regulatory element classes that bind both OCT4 and NANOG in Esrrbpositive cells. Class I elements lose NANOG and OCT4 binding in Esrrbnegative ESCs and associate with genes expressed preferentially in naïve ESCs. In contrast, Class II elements retain OCT4 but not NANOG binding in ESRRB-negative cells and associate with more broadly expressed genes. Therefore, mechanistic differences in TF function act cumulatively to restrict potency during exit from naïve pluripotency.

The Tbr2 Molecular Network Controls Cortical Neuronal Differentiation Through Complementary Genetic and Epigenetic Pathways

The T-box containing Tbr2 gene encodes for a transcription factor essential for the specification of the intermediate neural progenitors (INPs) originating the excitatory neurons of the cerebral cortex. However, its overall mechanism of action, direct target genes and cofactors remain unknown. Herein, we carried out global gene expression profiling combined with genome-wide binding site identification to determine the molecular pathways regulated by TBR2 in INPs. This analysis led to the identification of novel protein-protein interactions that control multiple features of INPs including cell-type identity, morphology, proliferation and migration dynamics. In particular, NEUROG2 and JMJD3 were found to associate with TBR2 revealing unexplored TBR2-dependent mechanisms. These interactions can explain, at least in part, the role of this transcription factor in the implementation of the molecular program controlling developmental milestones during corticogenesis. These data identify TBR2 as a major determinant of the INP-specific traits by regulating both genetic and epigenetic pathways.

EpiDenovo: a platform for linking regulatory de novo mutations to developmental epigenetics and diseases

De novo mutations (DNMs) have been shown to be a major cause of severe early-onset genetic disorders such as autism spectrum disorder and intellectual disability. Over one million DNMs have been identified in developmental disorders by next generation sequencing, but linking these DNMs to the genes that they impact remains a challenge, as the majority of them are embedded in non-coding regions. As most developmental diseases occur in the early stages of development or during childhood, it is crucial to clarify the details of epigenetic regulation in early development in order to interpret the mechanisms underlying developmental disorders. Here, we develop EpiDenovo, a database that is freely available at http://www.epidenovo.biols.ac.cn/, and which provides the associations between embryonic epigenomes and DNMs in developmental disorders, including several neuropsychiatric disorders and congenital heart disease. EpiDenovo provides an easy-to-use web interface allowing users rapidly to find the epigenetic signatures of DNMs and the expression patterns of the genes that they regulate during embryonic development. In summary, EpiDenovo is a useful resource for selecting candidate genes for further functional studies in embryonic development, and for investigating regulatory DNMs as well as other genetic variants causing or underlying developmental disorders.

Exploring Genetic Attributions Underlying Radiotherapy-Induced Fatigue in Prostate Cancer Patients

CONTEXT

Despite numerous proposed mechanisms, no definitive pathophysiology underlying radiotherapy-induced fatigue (RIF) has been established. However, the dysregulation of a set of 35 genes was recently validated to predict development of fatigue in prostate cancer patients receiving radiotherapy.

OBJECTIVES

To hypothesize novel pathways, and provide genetic targets for currently proposed pathways implicated in RIF development through analysis of the previously validated gene set.

METHODS

The gene set was analyzed for all phenotypic attributions implicated in the phenotype of fatigue. Initially, a "directed" approach was used by querying specific fatigue-related sub-phenotypes against all known phenotypic attributions of the gene set. Then, an "undirected" approach, reviewing the entirety of the literature referencing the 35 genes, was used to increase analysis sensitivity.

RESULTS

The dysregulated genes attribute to neural, immunological, mitochondrial, muscular, and metabolic pathways. In addition, certain genes suggest phenotypes not previously emphasized in the context of RIF, such as ionizing radiation sensitivity, DNA damage, and altered DNA repair frequency. Several genes also associated with prostate cancer depression, possibly emphasizing variable radiosensitivity by RIF-prone patients, which may have palliative care implications. Despite the relevant findings, many of the 35 RIF-predictive genes are poorly characterized, warranting their investigation.

CONCLUSION

The implications of herein presented RIF pathways are purely theoretical until specific end-point driven experiments are conducted in more congruent contexts. Nevertheless, the presented attributions are informative, directing future investigation to definitively elucidate RIF's pathoetiology. This study demonstrates an arguably comprehensive method of approaching known differential expression underlying a complex phenotype, to correlate feasible pathophysiology.

Two factor-based reprogramming of rodent and human fibroblasts into Schwann cells

Schwann cells (SCs) generate the myelin wrapping of peripheral nerve axons and are promising candidates for cell therapy. However, to date a renewable source of SCs is lacking. In this study, we show the conversion of skin fibroblasts into induced Schwann cells (iSCs) by driving the expression of two transcription factors, Sox10 and Egr2. iSCs resembled primary SCs in global gene expression profiling and PNS identity. In vitro, iSCs wrapped axons generating compact myelin sheaths with regular nodal structures. Conversely, iSCs from Twitcher mice showed a severe loss in their myelinogenic potential, demonstrating that iSCs can be an attractive system for in vitro modelling of PNS diseases. The same two factors were sufficient to convert human fibroblasts into iSCs as defined by distinctive molecular and functional traits. Generating iSCs through direct conversion of somatic cells offers opportunities for in vitro disease modelling and regenerative therapies.

Schwann cells (SCs) myelinate peripheral nerve axons and offer opportunities for the treatment of injuries and demyelinating diseases but reliable and renewable sources of these cells are hard to come by. Here the authors reprogram rat, mouse and human fibroblasts into Schwann cells using two transcription factors.

The ChIP-Seq tools and web server: a resource for analyzing ChIP-seq and other types of genomic data

BACKGROUND

ChIP-seq and related high-throughput chromatin profilig assays generate ever increasing volumes of highly valuable biological data. To make sense out of it, biologists need versatile, efficient and user-friendly tools for access, visualization and itegrative analysis of such data.

RESULTS

Here we present the ChIP-Seq command line tools and web server, implementing basic algorithms for ChIP-seq data analysis starting with a read alignment file. The tools are optimized for memory-efficiency and speed thus allowing for processing of large data volumes on inexpensive hardware. The web interface provides access to a large database of public data. The ChIP-Seq tools have a modular and interoperable design in that the output from one application can serve as input to another one. Complex and innovative tasks can thus be achieved by running several tools in a cascade.

CONCLUSIONS

The various ChIP-Seq command line tools and web services either complement or compare favorably to related bioinformatics resources in terms of computational efficiency, ease of access to public data and interoperability with other web-based tools. The ChIP-Seq server is accessible at http://ccg.vital-it.ch/chipseq/ .

GTRD: a database of transcription factor binding sites identified by ChIP-seq experiments

GTRD—Gene Transcription Regulation Database (http://gtrd.biouml.org)—is a database of transcription factor binding sites (TFBSs) identified by ChIP-seq experiments for human and mouse. Raw ChIP-seq data were obtained from ENCODE and SRA and uniformly processed: (i) reads were aligned using Bowtie2; (ii) ChIP-seq peaks were called using peak callers MACS, SISSRs, GEM and PICS; (iii) peaks for the same factor and peak callers, but different experiment conditions (cell line, treatment, etc.), were merged into clusters; (iv) such clusters for different peak callers were merged into metaclusters that were considered as non-redundant sets of TFBSs. In addition to information on location in genome, the sets contain structured information about cell lines and experimental conditions extracted from descriptions of corresponding ChIP-seq experiments. A web interface to access GTRD was developed using the BioUML platform. It provides: (i) browsing and displaying information; (ii) advanced search possibilities, e.g. search of TFBSs near the specified gene or search of all genes potentially regulated by a specified transcription factor; (iii) integrated genome browser that provides visualization of the GTRD data: read alignments, peaks, clusters, metaclusters and information about gene structures from the Ensembl database and binding sites predicted using position weight matrices from the HOCOMOCO database.

miR-34a Silences c-SRC to Attenuate Tumor Growth in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is an aggressive subtype with no clinically proven biologically targeted treatment options. The molecular heterogeneity of TNBC and lack of high frequency driver mutations other than TP53 have hindered the development of new and effective therapies that significantly improve patient outcomes. miRNAs, global regulators of survival and proliferation pathways important in tumor development and maintenance, are becoming promising therapeutic agents. We performed miRNA-profiling studies in different TNBC subtypes to identify miRNAs that significantly contribute to disease progression. We found that miR-34a was lost in TNBC, specifically within mesenchymal and mesenchymal stem cell-like subtypes, whereas expression of miR-34a targets was significantly enriched. Furthermore, restoration of miR-34a in cell lines representing these subtypes inhibited proliferation and invasion, activated senescence, and promoted sensitivity to dasatinib by targeting the proto-oncogene c-SRC. Notably, SRC depletion in TNBC cell lines phenocopied the effects of miR-34a reintroduction, whereas SRC overexpression rescued the antitumorigenic properties mediated by miR-34a. miR-34a levels also increased when cells were treated with c-SRC inhibitors, suggesting a negative feedback exists between miR-34a and c-SRC. Moreover, miR-34a administration significantly delayed tumor growth of subcutaneously and orthotopically implanted tumors in nude mice, and was accompanied by c-SRC downregulation. Finally, we found that miR-34a and SRC levels were inversely correlated in human tumor specimens. Together, our results demonstrate that miR-34a exerts potent antitumorigenic effects in vitro and in vivo and suggests that miR-34a replacement therapy, which is currently being tested in human clinical trials, represents a promising therapeutic strategy for TNBC.

The Tomato MIXTA-Like Transcription Factor Coordinates Fruit Epidermis Conical Cell Development and Cuticular Lipid Biosynthesis and Assembly

The epidermis of aerial plant organs is the primary source of building blocks forming the outer surface cuticular layer. To examine the relationship between epidermal cell development and cuticle assembly in the context of fruit surface, we investigated the tomato (Solanum lycopersicum) MIXTA-like gene. MIXTA/MIXTA-like proteins, initially described in snapdragon (Antirrhinum majus) petals, are known regulators of epidermal cell differentiation. Fruit of transgenically silenced SlMIXTA-like tomato plants displayed defects in patterning of conical epidermal cells. They also showed altered postharvest water loss and resistance to pathogens. Transcriptome and cuticular lipids profiling coupled with comprehensive microscopy revealed significant modifications to cuticle assembly and suggested SlMIXTA-like to regulate cutin biosynthesis. Candidate genes likely acting downstream of SlMIXTA-like included cytochrome P450s (CYPs) of the CYP77A and CYP86A subfamilies, LONG-CHAIN ACYL-COA SYNTHETASE2, GLYCEROL-3-PHOSPHATE SN-2-ACYLTRANSFERASE4, and the ATP-BINDING CASSETTE11 cuticular lipids transporter. As part of a larger regulatory network of epidermal cell patterning and L1-layer identity, we found that SlMIXTA-like acts downstream of SlSHINE3 and possibly cooperates with homeodomain Leu zipper IV transcription factors. Hence, SlMIXTA-like is a positive regulator of both cuticle and conical epidermal cell formation in tomato fruit, acting as a mediator of the tight association between fruit cutin polymer formation, cuticle assembly, and epidermal cell patterning.

Understanding gene regulatory mechanisms by integrating ChIP-seq and RNA-seq data: statistical solutions to biological problems

The availability of omic data produced from international consortia, as well as from worldwide laboratories, is offering the possibility both to answer long-standing questions in biomedicine/molecular biology and to formulate novel hypotheses to test. However, the impact of such data is not fully exploited due to a limited availability of multi-omic data integration tools and methods. In this paper, we discuss the interplay between gene expression and epigenetic markers/transcription factors. We show how integrating ChIP-seq and RNA-seq data can help to elucidate gene regulatory mechanisms. In particular, we discuss the two following questions: (i) Can transcription factor occupancies or histone modification data predict gene expression? (ii) Can ChIP-seq and RNA-seq data be used to infer gene regulatory networks? We propose potential directions for statistical data integration. We discuss the importance of incorporating underestimated aspects (such as alternative splicing and long-range chromatin interactions). We also highlight the lack of data benchmarks and the need to develop tools for data integration from a statistical viewpoint, designed in the spirit of reproducible research.