Biology subjects

Simon Anders

Publications and source records attributed to Simon Anders.

4 recordsLinked to original sources

bioRxiv2014

FourCSeq: Analysis of 4C sequencing data

1 Abstract 1.1 MotivationCircularized Chromosome Conformation Capture (4C) is a powerful technique for studying the spatial interactions of a specific genomic region called the \"viewpoint\" with the rest of the genome, both in a single condition or comparing different experimental conditions or cell types. Observed ligation frequencies show a strong, regular dependence on genomic distance from the viewpoint, on top of which specific interaction peaks are superimposed. Here, we address the computational task to find these specific interactions and to detect changes between interaction profiles of different conditions.\n\n1.2 ResultsWe model the overall trend of decreasing interaction frequency with genomic distance by fitting a smooth monotonously decreasing function to suitably transformed count data. Based on the fit, z-scores are calculated from the residuals, with high z scores being interpreted as peaks providing evidence for specific interactions. To compare different conditions, we normalize fragment counts between samples, and call for differential contact frequencies using the statistical method DESeq2 adapted from RNA-Seq analysis.\n\n1.3 Availability and ImplementationA full end-to-end analysis pipeline is implemented in the R package FourCSeq available at www.bioconductor.org.\n\n1.4 Contactfelix.klein@embl.de, whuber@embl.de

Bioinformatics↗

bioRxiv2014

Neural lineage induction reveals multi-scale dynamics of 3D chromatin organization

Regulation of gene expression underlies cell identity. Chromatin structure and gene activity are linked at multiple levels, via positioning of genomic loci to transcriptionally permissive or repressive environments and by connecting cis-regulatory elements such as promoters and enhancers. However, the genome-wide dynamics of these processes during cell differentiation has not been characterized. Using tethered chromatin conformation capture (TCC) sequencing we determined global three-dimensional chromatin structures in mouse embryonic stem (ES) and neural stem (NS) cell derivatives. We found that changes in the propensity of genomic regions to form inter-chromosomal contacts are pervasive in neural induction and are associated with the regulation of gene expression. Moreover, we found a pronounced contribution of euchromatic domains to the intra-chromosomal interaction network of pluripotent cells, indicating the existence of an ES cell-specific mode of chromatin organization. Mapping of promoter-enhancer interactions in pluripotent and differentiated cells revealed that spatial proximity without enhancer element activity is a common architectural feature in cells undergoing early developmental changes. Activity-independent formation of higher-order contacts between cis-regulatory elements, predominant at complex loci, may thus provide an additional layer of transcriptional control.

Molecular Biology↗

bioRxiv2014

HTSeq - A Python framework to work with high-throughput sequencing data

MotivationA large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard work flows, custom scripts are needed.\n\nResultsWe present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data such as genomic coordinates, sequences, sequencing reads, alignments, gene model information, variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes.\n\nAvailabilityHTSeq is released as open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index https://pypi.python.org/pypi/HTSeq.\n\nContactsanders@fs.tum.de

Bioinformatics↗

bioRxiv2014

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html.

Bioinformatics↗