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Andre Kahles

Publications and source records attributed to Andre Kahles.

3 recordsLinked to original sources

SplAdder: Identification, quantification and testing of alternative splicing events from RNA-Seq data

Motivation: Understanding the occurrence and regulation of alternative splicing (AS) is a key task towards explaining the regulatory processes that shape the complex transcriptomes of higher eukaryotes. With the advent of high-throughput sequencing of RNA (RNA-Seq), the diversity of AS transcripts could be measured at an unprecedented depth. Although the catalog of known AS events has grown ever since, novel transcripts are commonly observed when working with less well annotated organisms, in the context of disease, or within large populations. Whereas an identification of complete transcripts is technically challenging and computationally expensive, focusing on single splicing events as a proxy for transcriptome characteristics is fruitful and sufficient for a wide range of analyses.\n\nResults: We present SplAdder, an alternative splicing toolbox, that takes RNA-Seq alignments and an annotation file as input to i) augment the annotation based on RNA-Seq evidence, ii) identify alternative splicing events present in the augmented annotation graph, iii) quantify and confirm these events based on the RNA-Seq data, and iv) test for significant quantitative differences between samples. Thereby, our main focus lies on performance, accuracy and usability.\n\nAvailability: Source code and documentation are available for download at http://github.com/ratschlab/spladder. Example data, introductory information and a small tutorial are accessible via http://bioweb.me/spladder.\n\nContact: andre.kahles@ratschlab.org, gunnar.ratsch@ratschlab.org

Bioinformatics

MMR: A Tool for Read Multi-Mapper Resolution

MotivationMapping high throughput sequencing data to a reference genome is an essential step for most analysis pipelines aiming at the computational analysis of genome and transcriptome sequencing data. Breaking ties between equally well mapping locations poses a severe problem not only during the alignment phase, but also has significant impact on the results of downstream analyses. We present the multimapper resolution (MMR) tool that infers optimal mapping locations from the coverage density of other mapped reads.\n\nResultsFiltering alignments with MMR can significantly improve the performance of downstream analyses like transcript quantitation and differential testing. We illustrate that the accuracy (Spear-man correlation) of transcript quantification increases by 17% when using reads of length 51. In addition, MMR decreases the alignment file sizes by more than 50% and this leads to a reduced running time of the quantification tool. Our efficient implementation of the MMR algorithm is easily applicable as a post-processing step to existing alignment files in BAM format. Its complexity scales linearly with the number of alignments and requires no further inputs.\n\nSupplementary MaterialSource code and documentation are available for download at github.com/ratschlab/mmr. Supplementary text and figures, comprehensive testing results and further information can be found at bioweb.me/mmr.\n\nContactakahles@cbio.mskcc.org and raetsch@cbio.mskcc.org

Bioinformatics

Integrative Genome-wide Analysis of the Determinants of RNA Splicing in Kidney Renal Clear Cell Carcinoma

We present a genome-wide analysis of splicing patterns of 282 kidney renal clear cell carcinoma patients in which we integrate data from whole-exome sequencing of tumor and normal samples, RNA-seq and copy number variation. We proposed a scoring mechanism to compare splicing patterns in tumor samples to normal samples in order to rank and detect tumor-specific isoforms that have a potential for new biomarkers. We identified a subset of genes that show introns only observable in tumor but not in normal samples, ENCODE and GEUVADIS samples. In order to improve our understanding of the underlying genetic mechanisms of splicing variation we performed a large-scale association analysis to find links between somatic or germline variants with alternative splicing events. We identified 915 cis- and trans-splicing quantitative trait loci (sQTL) associated with changes in splicing patterns. Some of these sQTL have previously been associated with being susceptibility loci for cancer and other diseases. Our analysis also allowed us to identify the function of several COSMIC variants showing significant association with changes in alternative splicing. This demonstrates the potential significance of variants affecting alternative splicing events and yields insights into the mechanisms related to an array of disease phenotypes.

Cancer Biology