Genomic Coordinates Research Articles

Anchored pseudo-de novo assembly of human genomes identifies extensive sequence variation from unmapped sequence reads.

The human genome reference (HGR) completion marked the genomics era beginning, yet despite its utility universal application is limited by the small number of individuals used in its development. This is highlighted by the presence of high-quality sequence reads failing to map within the HGR. Sequences failing to map generally represent 2-5% of total reads, which may harbor regions that would enhance our understanding of population variation, evolution, and disease. Alternatively, complete de novo assemblies can be created, but these effectively ignore the groundwork of the HGR. In an effort to find a middle ground, we developed a bioinformatic pipeline that maps paired-end reads to the HGR as separate single reads, exports unmappable reads, de novo assembles these reads per individual and then combines assemblies into a secondary reference assembly used for comparative analysis. Using 45 diverse 1000 Genomes Project individuals, we identified 351,361 contigs covering 195.5Mb of sequence unincorporated in GRCh38. 30,879 contigs are represented in multiple individuals with ~40% showing high sequence complexity. Genomic coordinates were generated for 99.9%, with 52.5% exhibiting high-quality mapping scores. Comparative genomic analyses with archaic humans and primates revealed significant sequence alignments and comparisons with model organism RefSeq gene datasetsidentified novel human genes. If incorporated, these sequences will expand the HGR, but more importantly our data highlight that with this method low coverage (~10-20×) next-generation sequencing can still be used to identify novel unmapped sequences to explore biological functions contributing to human phenotypic variation, disease and functionality for personal genomic medicine.

Statistical Enrichment of Epigenetic States Around Triplet Repeats that Can Undergo Expansions.

More than 30 human genetic diseases are linked to tri-nucleotide repeat expansions. There is no known mechanism that explains repeat expansions in full, but changes in the epigenetic state of the associated locus has been implicated in the disease pathology for a growing number of examples. A comprehensive comparative analysis of the genomic features associated with diverse repeat expansions has been lacking. Here, in an effort to decipher the propensity of repeats to undergo expansion and result in a disease state, we determine the genomic coordinates of tri-nucleotide repeat tracts at base pair resolution and computationally establish epigenetic profiles around them. Using three complementary statistical tests, we reveal that several epigenetic states are enriched around repeats that are associated with disease, even in cells that do not harbor expansion, relative to a carefully stratified background. Analysis of over one hundred cell types reveals that epigenetic states generally tend to vary widely between genic regions and cell types. However, there is qualified consistency in the epigenetic signatures of repeats associated with disease suggesting that changes to the chromatin and the DNA around an expanding repeat locus are likely to be similar. These epigenetic signatures may be exploited further to develop models that could explain the propensity of repeats to undergo expansions.

Microdissection of lampbrush chromosomes as an approach for generation of locus-specific FISH-probes and samples for high-throughput sequencing.

BackgroundOver the past two decades, chromosome microdissection has been widely used in diagnostics and research enabling analysis of chromosomes and their regions through probe generation and establishing of chromosome- and chromosome region-specific DNA libraries. However, relatively small physical size of mitotic chromosomes limited the use of the conventional chromosome microdissection for investigation of tiny chromosomal regions.ResultsIn the present study, we developed a workflow for mechanical microdissection of giant transcriptionally active lampbrush chromosomes followed by the preparation of whole-chromosome and locus-specific fluorescent in situ hybridization (FISH)-probes and high-throughput sequencing. In particular, chicken (Gallus g. domesticus) lampbrush chromosome regions as small as single chromomeres, individual lateral loops and marker structures were successfully microdissected. The dissected fragments were mapped with high resolution to target regions of the corresponding lampbrush chromosomes. For investigation of RNA-content of lampbrush chromosome structures, samples retrieved by microdissection were subjected to reverse transcription. Using high-throughput sequencing, the isolated regions were successfully assigned to chicken genome coordinates. As a result, we defined precisely the loci for marker structures formation on chicken lampbrush chromosomes 2 and 3. Additionally, our data suggest that large DAPI-positive chromomeres of chicken lampbrush chromosome arms are characterized by low gene density and high repeat content.ConclusionsThe developed technical approach allows to obtain DNA and RNA samples from particular lampbrush chromosome loci, to define precisely the genomic position, extent and sequence content of the dissected regions. The data obtained demonstrate that lampbrush chromosome microdissection provides a unique opportunity to correlate a particular transcriptional domain or a cytological structure with a known DNA sequence. This approach offers great prospects for detailed exploration of functionally significant chromosomal regions.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2437-4) contains supplementary material, which is available to authorized users.

G2pDB: A Database Mapping Protein Post-Translational Modifications to Genomic Coordinates.

Large scale proteomics have made it possible to broadly screen samples for the presence of many types of post-translational modifications, such as phosphorylation, acetylation, and ubiquitination. This type of data has allowed the localization of these modifications to either a specific site on a proteolytically generated peptide or to within a small domain on the peptide. The resulting modification acceptor sites can then be mapped onto the appropriate protein sequences and the information archived. This paper describes the usage of a very large archive of experimental observations of human post-translational modifications to create a map of the most reproducible modification observations onto the complete set of human protein sequences. This set of modification acceptor sites was then directly translated into the genomic coordinates for the codons for the residues at those sites. We constructed the database g2pDB using this protein-to-codon site mapping information. The information in g2pDB has been made available through a RESTful-style API, allowing researchers to determine which specific protein modifications would be perturbed by a set of observed nucleotide variants determined by high throughput DNA or RNA sequencing.

Seqinspector: position-based navigation through the ChIP-seq data landscape to identify gene expression regulators.

BackgroundThe regulation of gene expression in eukaryotic cells is a complex process that involves epigenetic modifications and the interaction of DNA with multiple transcription factors. This process can be studied with unprecedented sensitivity using a combination of chromatin immunoprecipitation and next-generation DNA sequencing (ChIP-seq). Available ChIP-seq data can be further utilized to interpret new gene expression profiling experiments.ResultsHere, we describe seqinspector, a tool that accepts any set of genomic coordinates from ChIP-seq or RNA-seq studies to identify shared transcriptional regulators. The presented web resource includes a large collection of publicly available ChIP-seq and RNA-seq experiments (>1300 tracks) performed on transcription factors, histone modifications, RNA polymerases, enhancers and insulators in humans and mice. Over-representation is calculated based on the coverage computed directly from indexed files storing ChIP-seq data (bigwig). Therefore, seqinspector is not limited to pre-computed sets of gene promoters.ConclusionThe tool can be used to identify common gene expression regulators for sets of co-expressed transcripts (including miRNAs, lncRNAs or any novel unannotated RNAs) or for sets of ChIP-seq peaks to identify putative protein-protein interactions or transcriptional co-factors. The tool is available at http://seqinspector.cremag.org.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-0938-4) contains supplementary material, which is available to authorized users.

MTD: a mammalian transcriptomic database to explore gene expression and regulation.

A systematic transcriptome survey is essential for the characterization and comprehension of the molecular basis underlying phenotypic variations. Recently developed RNA-seq methodology has facilitated efficient data acquisition and information mining of transcriptomes in multiple tissues/cell lines. Current mammalian transcriptomic databases are either tissue-specific or species-specific, and they lack in-depth comparative features across tissues and species. Here, we present a mammalian transcriptomic database (MTD) that is focused on mammalian transcriptomes, and the current version contains data from humans, mice, rats and pigs. Regarding the core features, the MTD browses genes based on their neighboring genomic coordinates or joint KEGG pathway and provides expression information on exons, transcripts and genes by integrating them into a genome browser. We developed a novel nomenclature for each transcript that considers its genomic position and transcriptional features. The MTD allows a flexible search of genes or isoforms with user-defined transcriptional characteristics and provides both table-based descriptions and associated visualizations. To elucidate the dynamics of gene expression regulation, the MTD also enables comparative transcriptomic analysis in both intraspecies and interspecies manner. The MTD thus constitutes a valuable resource for transcriptomic and evolutionary studies. The MTD is freely accessible at http://mtd.cbi.ac.cn.

Holliday Junctions Are Associated with Transposable Element Sequences in the Human Genome

Holliday junctions (HJs) constitute important intermediate structures for many cell functions such as DNA recombination and DNA repair. They derive from a 10-nt degenerate sequence, with a 3-nt core motif. In this study, we explored the human genome whether the HJ degenerate sequence associates with transposable elements (TEs) and mainly with those of the active and inactive ALU, LINE, SVA and HERV families. We identified six different forms of the HJ sequence motif, and we located the genomic coordinates of sequences containing both HJs and TEs. From 2982 total HJs, a significant number of 1319 TE-associated HJs were found, with a median distribution of 1 per 2.4Mb. The HJs with higher GC content were observed more frequently at the genome. A high percentage of HJs were associated with all main TE families, with specificity for particular active or inactive elements: DNA elements and the retroelements ALUs, LINEs and HERVs up to 41.94%, 72.72%, 42.94% and 84.5%, respectively. Phylogenetic analysis revealed that HJs occur in both active and inactive TEs. Furthermore, the TE-associated HJs were almost exclusively found within a distance less than 1Mb from human genes, while only 23 were not associated with any genes. This is the first report associating human HJs, with mobile elements. Our data pinpoint that particular HJ forms show preference for specific active retrotransposon families of ALUs and LINEs, suggesting that retrotransposon-incorporated HJs may relocate or replicate in the genome through retrotransposition, contributing to recombination, genome plasticity and DNA repair.

SNPTracker: A Swift Tool for Comprehensive Tracking and Unifying dbSNP rs IDs and Genomic Coordinates of Massive Sequence Variants.

The reference single nucleotide polymorphism (rs) ID in dbSNP (http://www.ncbi.nlm.nih.gov/SNP/) is a key resource identifier, which is widely used in human genetics and genomics studies. However, its application is often complicated by the varied IDs of different versions. Here, we developed a user-friendly tool, SNPTracker, for comprehensively tracking and unifying the rs IDs and genomic coordinates of massive sequence variants at a time. It worked perfectly, and had much higher accuracy and capacity than two alternative utilities in our proof-of-principle examples. SNPTracker will greatly facilitate genetic data exchange and integration in the postgenome-wide association study era.

Discrepancies between human DNA, mRNA and protein reference sequences and their relation to single nucleotide variants in the human population.

The protein coding sequences of the human reference genome GRCh38, RefSeq mRNA and UniProt protein databases are sometimes inconsistent with each other, due to polymorphisms in the human population, but the overall landscape of the discordant sequences has not been clarified. In this study, we comprehensively listed the discordant bases and regions between the GRCh38, RefSeq and UniProt reference sequences, based on the genomic coordinates of GRCh38. We observed that the RefSeq sequences are more likely to represent the major alleles than GRCh38 and UniProt, by assigning the alternative allele frequencies of the discordant bases. Since some reference sequences have minor alleles, functional and structural annotations may be performed based on rare alleles in the human population, thereby biasing these analyses. Some of the differences between the RefSeq and GRCh38 account for biological differences due to known RNA-editing sites. The definitions of the coding regions are frequently complicated by possible micro-exons within introns and by SNVs with large alternative allele frequencies near exon–intron boundaries. The mRNA or protein regions missing from GRCh38 were mainly due to small deletions, and these sequences need to be identified. Taken together, our results clarify overall consistency and remaining inconsistency between the reference sequences.

Extent of genome-wide linkage disequilibrium in Pinzgau cattle

The aim of this study was to describe the extent of linkage disequilibrium (LD) based on genotyping data in Pinzgau cattle population. DNA samples were obtained from 19 bulls of active Pinzgau cattle population in Slovakia. Genotyping was carried out in commercial lab using an Illumina BovineSNP50 BeadChip. The genotyping array contained 54,609 single nucleotide polymorphisms (SNPs). After quality control SNPs localized on sex chromosomes were excluded. A total of 42,248 (79.97%) SNPs were found to be polymorphic. The distribution of SNPs over entire genomes of all chromosomes was not uniform. The minor allele frequencies across autosomal loci ranged from 0.266 (BTA4) to 0.280 (BTA23) with average value 0.273±0.133. Only adjacent SNPs with distance less than 5 Mb and LD (r2) values from 0.01 to 0.99 were used in the characterization of LD extent. After filtering, the genome coordinates of 31,063 SNP markers covered all regions of autosomes on the length 2519232 kb. The spacing across genome between adjacent SNPs was in average 46.89±47.48 kb and within autosomes ranged from 43.65 kb (BTA25) to 52.59 kb (BTA5). The distribution of SNPs on different chromosomes ranged from 633 (79.13%) on BTA29 to 1123 (83.62%) on BTA14. The average LD between adjacent markers within autosomes reached values from 0.189 to 0.234 for chromosomes BTA 29 and 21, respectively. Division of r2-values over all 29 autosomes were performed according to their physical intermarker distances and averaged within the groups for analysis of distance effect on LD. The results of our study indicated the rapid decay of LD with increasing distance between markers. Further investigation will be oriented on evaluation of effective population size based on LD data. This could allow improvement of our knowledge about genetic diversity and its use for breed preservation of original phenotype supported by the current selection schemes and breeding programmes.

PGx: Putting Peptides to BED.

Every molecular player in the cast of biology’s central dogma is being sequenced and quantified with increasing ease and coverage. To bring the resulting genomic, transcriptomic, and proteomic data sets into coherence, tools must be developed that do not constrain data acquisition and analytics in any way but rather provide simple links across previously acquired data sets with minimal preprocessing and hassle. Here we present such a tool: PGx, which supports proteogenomic integration of mass spectrometry proteomics data with next-generation sequencing by mapping identified peptides onto their putative genomic coordinates.

DNAshapeR: an R/Bioconductor package for DNA shape prediction and feature encoding.

Summary: DNAshapeR predicts DNA shape features in an ultra-fast, high-throughput manner from genomic sequencing data. The package takes either nucleotide sequence or genomic coordinates as input and generates various graphical representations for visualization and further analysis. DNAshapeR further encodes DNA sequence and shape features as user-defined combinations of k-mer and DNA shape features. The resulting feature matrices can be readily used as input of various machine learning software packages for further modeling studies.Availability and implementation: The DNAshapeR software package was implemented in the statistical programming language R and is freely available through the Bioconductor project at https://www.bioconductor.org/packages/devel/bioc/html/DNAshapeR.html and at the GitHub developer site, http://tsupeichiu.github.io/DNAshapeR/.Contact: rohs@usc.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Comprehensive red blood cell and platelet antigen prediction from whole genome sequencing: proof of principle.

BACKGROUNDThere are 346 serologically defined red blood cell (RBC) antigens and 33 serologically defined platelet (PLT) antigens, most of which have known genetic changes in 45 RBC or six PLT genes that correlate with antigen expression. Polymorphic sites associated with antigen expression in the primary literature and reference databases are annotated according to nucleotide positions in cDNA. This makes antigen prediction from next‐generation sequencing data challenging, since it uses genomic coordinates.STUDY DESIGN AND METHODSThe conventional cDNA reference sequences for all known RBC and PLT genes that correlate with antigen expression were aligned to the human reference genome. The alignments allowed conversion of conventional cDNA nucleotide positions to the corresponding genomic coordinates. RBC and PLT antigen prediction was then performed using the human reference genome and whole genome sequencing (WGS) data with serologic confirmation.RESULTSSome major differences and alignment issues were found when attempting to convert the conventional cDNA to human reference genome sequences for the following genes: ABO, A4GALT, RHD, RHCE, FUT3, ACKR1 (previously DARC), ACHE, FUT2, CR1, GCNT2, and RHAG. However, it was possible to create usable alignments, which facilitated the prediction of all RBC and PLT antigens with a known molecular basis from WGS data. Traditional serologic typing for 18 RBC antigens were in agreement with the WGS‐based antigen predictions, providing proof of principle for this approach.CONCLUSIONDetailed mapping of conventional cDNA annotated RBC and PLT alleles can enable accurate prediction of RBC and PLT antigens from whole genomic sequencing data.

Integrated Analysis of the Human Hematopoietic Non-Coding RNA Landscape Reveals Lnc-RNA Stem Cell Signature in AML

Long non-coding RNAs (lncRNAs) and miRNAs have emerged as crucial regulators of gene expression, epigenetics and cell fate decisions. Here we present an integrated quantitative and functional analysis of the miRNA-, lncRNA- and mRNA-transcriptome of purified human hematopoietic stem cells (HSCs) and their differentiated progenies, including granulocytes, monocytes, T-cells, NK-cells, B-cells, megakaryocytes and erythroid precursors, which we correlated with the ncRNA expression profile of 46 pediatric AML samples to establish a core lncRNA stem cell signature in AML.For each blood cell population, RNA from 5 healthy donors was hybridized onto three microarray platforms (Arraystar lncRNA V2.0, NCode™-miRNA/-ncRNA), yielding a coverage of more than 40,000 lncRNAs, 25,000 mRNAs and 900 miRNAs on 146 arrays. Compared to mRNAs, the mean expression level of lncRNAs was nearly 2-fold lower (p<2.2x10-16), highlighting the challenge for RNA-Seq to provide adequate coverage of these rare transcripts. Hierarchical clustering, PCA (principle components analysis) and t-SNE (t-distributed stochastic neighbor embedding) on lncRNA and miRNA genes robustly structured the dataset into groups of samples that matched the input populations, demonstrating their unique ncRNA expression profiles. Using LIMMA (linear models for microarrays) and PAM (prediction analysis of microarrays) we extracted a robust lncRNA/miRNA core signature for each population. Self-organizing maps (SOM) structured the gene space into spot clusters of lncRNAs and mRNAs that were coordinately expressed during maturation from HSCs into the different lineages. Using a guilt-by-association approach, we assigned potential functionality to lncRNAs through gene set enrichment analysis of co-expressed mRNAs (validated for a set of lncRNAs by qRT-PCR). To demonstrate their functionality, we knocked down two lncRNAs (LINC00173 and DY635655) from the granulocytic core signature using two independent shRNA constructs, which resulted in diminished granulocytic in vitro differentiation (2-fold reduction in percentage of CD66b+/CD13+ granulocytes, p≤0.05), myeloid colony-formation (1.5-2-fold, p≤0.05) and nuclear lobulation (MGG-staining). Accordingly, CRISPR-mediated transcriptional repression of nuclear localized LINC00173 and cytoplasmic localized DY635655 (RNA-FISH validated by qRT-RNA of fractionated RNA) using dCas9-KRAB and three sgRNAs per locus reduced proliferation of myeloid NB4 cells (2-3-fold, p≤0.01).By plotting ncRNA expression over the genomic coordinates of all chromosomes, we uncovered a strong and highly coordinated upregulation of miRNAs, small nucleolar RNAs (snoRNAs) and lncRNAs (MEG3, MEG8 and MEG9) within the DLK1-DIO3 locus on chromosome 14 (hsa14) specifically in megakaryocytes (average log2-FC >5, p<10-8; confirmed by qRT-PCR). shRNA-mediated knock-down of MEG3 reduced erythroid colony-formation and megakaryocytic cell proliferation in vitro (p≤0.05), implicating the functional importance of this ncRNA locus.Having established a global human hematopoietic lncRNA expression resource, we extended our findings to malignant hematopoiesis. Linear (PCA) and nonlinear (t-SNE) dimensionality reduction of 46 pediatric AML samples including Down syndrome AMKL, core-binding factor AMLs (inv[16] or t[8;21]) and MLL-rearranged leukemias mapped most samples to a space between HSCs and differentiated cells together with the myeloid progenitors. A subset of AML-samples mapped closely to healthy HSCs, including most of the DS-AMKLs and MLL-AMLs. We identified a stem-cell associated lncRNA signature that was absent in healthy differentiated progenies, but upregulated in AML samples. Interestingly, AML samples that did not show upregulation of this set of lncRNAs suppressed the upregulation of differentiation-associated lncRNAs, thereby keeping the global identity of myeloid progenitors as shown by t-SNE.Thus, our study provides a comprehensive resource for the exploration of the mRNA-, lncRNA- and miRNA-transcriptome across the human hematopoietic hierarchy, including malignant hematopoiesis. The definition of a core lncRNA stem cell signature in normal HSCs and AML blasts will guide our way towards an improved understanding of self-renewal and the underlying transcriptional program, which is hijacked during malignant transformation. DisclosuresNo relevant conflicts of interest to declare.

Non-random fragmentation patterns in circulating cell-free DNA reflect epigenetic regulation.

BackgroundThe assessment of cell-free circulating DNA fragments, also known as a "liquid biopsy" of the patient's plasma, is an important source for the discovery and subsequent non-invasive monitoring of cancer and other pathological conditions. Although the nucleosome-guided fragmentation patterns of cell-free DNA (cfDNA) have not yet been studied in detail, non-random representation of cfDNA sequencies may reflect chromatin features in the tissue of origin at gene-regulation level.ResultsIn this study, we investigated the association between epigenetic landscapes of human tissues evident in the patterns of cfDNA in plasma by deep sequencing of human cfDNA samples. We have demonstrated that baseline characteristics of cfDNA fragmentation pattern are in concordance with the ones corresponding to cell lines-derived. To identify the loci differentially represented in cfDNA fragment, we mapped the transcription start sites within the sequenced cfDNA fragments and tested for association of these genomic coordinates with the relative strength and the patterns of gene expressions. Preselected sets of house-keeping and tissue specific genes were used as models for actively expressed and silenced genes. Developed measure of gene regulation was able to differentiate these two sets based on sequencing coverage near gene transcription start site.ConclusionExperimental outcomes suggest that cfDNA retains characteristics previously noted in genome-wide analysis of chromatin structure, in particular, in MNase-seq assays. Thus far the analysis of the DNA fragmentation pattern may aid further developing of cfDNA based biomarkers for a variety of human conditions.

Corrigendum to "Congenital microcephaly and chorioretinopathy due to de novo heterozygous KIF11 mutations: Five novel mutations and review of the literature. Am J Med Genet Part A 2014 164A:2879-86".

In Tables I and II in the article, there are a few errors in the genomic coordinates and mutation nomenclature. Revised Table II lists the mutations with the correct nomenclature. Supplementary Figure I shows representative Sanger sequencing chromatograms of these mutations. The authors regret these errors. These mutations have been submitted to the LOVD KIF11 database (http://databases.lovd.nl/shared/variants/KIF11). The individual IDs of these mutations are 47821, 47847, 47849, 47850 and 47851. Additional supporting information may be found in the online version of this article at the publisher's web-site. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

HPMV: human protein mutation viewer - relating sequence mutations to protein sequence architecture and function changes.

Next-generation sequencing advances are rapidly expanding the number of human mutations to be analyzed for causative roles in genetic disorders. Our Human Protein Mutation Viewer (HPMV) is intended to explore the biomolecular mechanistic significance of non-synonymous human mutations in protein-coding genomic regions. The tool helps to assess whether protein mutations affect the occurrence of sequence-architectural features (globular domains, targeting signals, post-translational modification sites, etc.). As input, HPMV accepts protein mutations - as UniProt accessions with mutations (e.g. HGVS nomenclature), genome coordinates, or FASTA sequences. As output, HPMV provides an interactive cartoon showing the mutations in relation to elements of the sequence architecture. A large variety of protein sequence architectural features were selected for their particular relevance to mutation interpretation. Clicking a sequence feature in the cartoon expands a tree view of additional information including multiple sequence alignments of conserved domains and a simple 3D viewer mapping the mutation to known PDB structures, if available. The cartoon is also correlated with a multiple sequence alignment of similar sequences from other organisms. In cases where a mutation is likely to have a straightforward interpretation (e.g. a point mutation disrupting a well-understood targeting signal), this interpretation is suggested. The interactive cartoon can be downloaded as standalone viewer in Java jar format to be saved and viewed later with only a standard Java runtime environment. The HPMV website is: http://hpmv.bii.a-star.edu.sg/ .

The Genetic Modifiers of Motor OnsetAge (GeM MOA) Website: Genome-wide Association Analysis for Genetic Modifiers of Huntington’s Disease

Huntington's disease (HD) is a dominantly inherited disease caused by a CAG expansion mutation in HTT. The age at onset of clinical symptoms is determined primarily by the length of this CAG expansion but is also influenced by other genetic and/or environmental factors. Recently, through genome-wide association studies (GWAS) aimed at discovering genetic modifiers, we identified loci associated with age at onset of motor signs that are significant at the genome-wide level. However, many additional HD modifiers may exist but may not have achieved statistical significance due to limited power. In order to disseminate broadly the entire GWAS results and make them available to complement alternative approaches, we have developed the internet website "GeM MOA" where genetic association results can be searched by gene name, SNP ID, or genomic coordinates of a region of interest. Users of the Genetic Modifiers of Motor Onset Age (GeM MOA) site can therefore examine support for association between any gene region and age at onset of HD motor signs. GeM MOA's interactive interface also allows users to navigate the surrounding region and to obtain association p-values for individual SNPs. Our website conveys a comprehensive view of the genetic landscape of modifiers of HD from the existing GWAS, and will provide the means to evaluate the potential influence of genes of interest on the onset of HD. GeM MOA is freely available at https://www.hdinhd.org/.

CompGO: an R package for comparing and visualizing Gene Ontology enrichment differences between DNA binding experiments.

BackgroundGene ontology (GO) enrichment is commonly used for inferring biological meaning from systems biology experiments. However, determining differential GO and pathway enrichment between DNA-binding experiments or using the GO structure to classify experiments has received little attention.ResultsHerein, we present a bioinformatics tool, CompGO, for identifying Differentially Enriched Gene Ontologies, called DiEGOs, and pathways, through the use of a z-score derivation of log odds ratios, and visualizing these differences at GO and pathway level. Through public experimental data focused on the cardiac transcription factor NKX2-5, we illustrate the problems associated with comparing GO enrichments between experiments using a simple overlap approach.ConclusionsWe have developed an R/Bioconductor package, CompGO, which implements a new statistic normally used in epidemiological studies for performing comparative GO analyses and visualizing comparisons from .BED data containing genomic coordinates as well as gene lists as inputs. We justify the statistic through inclusion of experimental data and compare to the commonly used overlap method. CompGO is freely available as a R/Bioconductor package enabling easy integration into existing pipelines and is available at: http://www.bioconductor.org/packages/release/bioc/html/CompGO.html packages/release/bioc/html/CompGO.html

RNA-Seq Analysis of Differential Splice Junction Usage and Intron Retentions by DEXSeq.

Alternative splicing is an important biological process in the generation of multiple functional transcripts from the same genomic sequences. Differential analysis of splice junctions (SJs) and intron retentions (IRs) is helpful in the detection of alternative splicing events. In this study, we conducted differential analysis of SJs and IRs by use of DEXSeq, a Bioconductor package originally designed for differential exon usage analysis in RNA-seq data analysis. We set up an analysis pipeline including mapping of RNA-seq reads, the preparation of count tables of SJs and IRs as the input files, and the differential analysis in DEXSeq. We analyzed the public RNA-seq datasets generated from RNAi experiments on Drosophila melanogaster S2-DRSC cells to deplete RNA-binding proteins (GSE18508). The analysis confirmed previous findings on the alternative splicing of the trol and Ant2 (sesB) genes in the CG8144 (ps)-depletion experiment and identified some new alternative splicing events in other RNAi experiments. We also identified IRs that were confirmed in our SJ analysis. The proposed method used in our study can output the genomic coordinates of differentially used SJs and thus enable sequence motif search. Sequence motif search and gene function annotation analysis helped us infer the underlying mechanism in alternative splicing events. To further evaluate this method, we also applied the method to public RNA-seq data from human breast cancer (GSE45419) and the plant Arabidopsis (SRP008262). In conclusion, our study showed that DEXSeq can be adapted to differential analysis of SJs and IRs, which will facilitate the identification of alternative splicing events and provide insights into the molecular mechanisms of transcription processes and disease development.