RiboDiff: detecting changes of mRNA translation efficiency from ribosome footprints

MOTIVATION: Deep sequencing based ribosome footprint profiling can provide novel insights into the regulatory mechanisms of protein translation. However, the observed ribosome profile is fundamentally confounded by transcriptional activity. In order to decipher principles of translation regulation, tools that can reliably detect changes in translation efficiency in case-control studies are needed. RESULTS: We present a statistical framework and an analysis tool, RiboDiff, to detect genes with changes in translation efficiency across experimental treatments. RiboDiff uses generalized linear models to estimate the over-dispersion of RNA-Seq and ribosome profiling measurements separately, and performs a statistical test for differential translation efficiency using both mRNA abundance and ribosome occupancy

Alternative splicing substantially diversifies the transcriptome during early photomorphogenesis and correlates with the energy availability in arabidopsis

Plants use light as source of energy and information to detect diurnal rhythms and seasonal changes. Sensing changing light conditions is critical to adjust plant metabolism and to initiate developmental transitions. Here we analyzed transcriptome-wide alterations in gene expression and alternative splicing (AS) of etiolated seedlings undergoing photomorphogenesis upon exposure to blue, red, or white light. Our analysis revealed massive transcriptome reprograming as reflected by differential expression of ~20% of all genes and changes in several hundred AS events. For more than 60% of all regulated AS events, light promoted the production of a presumably protein-coding variant at the expense of an mRNA with nonsense-mediated decay-triggering features. Accordingly, AS of the putative splicing factor REDUCED RED-LIGHT RESPONSES IN CRY1CRY2 BACKGROUND 1 (RRC1), previously identified as a red light signaling component, was shifted to the functional variant under light. Downstream analyses of candidate AS events pointed at a role of photoreceptor signaling only in monochromatic but not in white light. Furthermore, we demonstrated similar AS changes upon light exposure and exogenous sugar supply, with a critical involvement of kinase signaling. We propose that AS is an integration point of signaling pathways that sense and transmit information regarding the energy availability in plants

An Introduction to Kernel Methods for Classification, Regression and the Analysis of Structured Data

Kernel methods have become very popular in machine learning research and many fields of applications. This tutorial will introduce kernels, their basic properties and methods which take advantage of them. We will use real world problems from computational biology and beyond as examples to illustrate how do select and engineer an appropriate kernel function. This tutorial will begin with a presentation of kernel methods and their properties. This will be followed by an introduction to the theory of support vector algorithms such as support vector machines, support vector regression and kernel principal component analysis. We will also briefly discuss optimization techniques to obtain solutions and discuss variations such as v-SVMs or C-SVMs. We will also discuss how kernel methods can be used for structured output prediction and nonparametric statistical inference. In the last part, we will show how kernel methods can be applied to problems in computational biology

Splice Form Prediction using Machine Learning

Accurate ab initio gene finding is still a major challenge in computational biology. We employ cutting edge machine learning similar to Hidden-Markov-SVMs to assay and improve the accuracy of genome annotations. We applied our system on the C_elegans genome and were able to drastically improve its annotation

Splice Form Prediction using Machine Learning

Accurate ab initio gene finding is still a major challenge in computational biology. We employ cutting edge machine learning similar to Hidden-Markov-SVMs to assay and improve the accuracy of genome annotations. We applied our system on the C_elegans genome and were able to drastically improve its annotation

Talsperren-Kapazitaetserweiterung Ueber die Kapazitaetserweiterung von Talsperren

SIGLEDEGerman

Fast and Accurate RNA-Seq alignments with PALMapper

Short mRNA sequences produced by RNA-Seq enhance transcriptome analysis and promise great opportunities for the discovery of new genes and the identification of alternative transcripts. However, the sheer amount of high throughput sequencing data requires efficient methods for accurate spliced alignments of reads against the reference genome, which is further challenged by size and quality of the sequence reads. We present an original RNA-Seq read mapper, called PALMapper, that combines a faster extension of the high accurate alignment method QPALMA with the fast short read aligner GenomeMapper. PALMapper quickly carries out an initial read mapping which then guides a Banded Semi-Global alignment algorithm that allows for long gaps corresponding to introns. PALMapper drastically improves the speed of QPALMA (around 50 times faster) and still computes both spliced and unspliced alignments at high accuracy by taking advantage of base quality information and computational splice site predictions. Moreover, PALMapper is under active development and offers a growing pool of features such as polyA trimming or non-canonical splice site support, which can improve again alignment accuracy for specific downstream studies. Finally, PALMapper does not rely on any annotation but is able to remap reads against an inferred splice junction database. This strategy applied on simulated data from C. elegans increases the number of correct spliced alignments from 89% to 92% while the incorrect alignments decrease by 27%. On the same dataset, we show that PALMapper outperfoms GSNAP and TopHat, two other widely used alignment tools