GPUmotif: An Ultra-Fast and Energy-Efficient Motif Analysis Program Using Graphics Processing Units

Computational detection of TF binding patterns has become an indispensable tool in functional genomics research. With the rapid advance of new sequencing technologies, large amounts of protein-DNA interaction data have been produced. Analyzing this data can provide substantial insight into the mechanisms of transcriptional regulation. However, the massive amount of sequence data presents daunting challenges. In our previous work, we have developed a novel algorithm called Hybrid Motif Sampler (HMS) that enables more scalable and accurate motif analysis. Despite much improvement, HMS is still time-consuming due to the requirement to calculate matching probabilities position-by-position. Using the NVIDIA CUDA toolkit, we developed a graphics processing unit (GPU)-accelerated motif analysis program named GPUmotif. We proposed a “fragmentation" technique to hide data transfer time between memories. Performance comparison studies showed that commonly-used model-based motif scan and de novo motif finding procedures such as HMS can be dramatically accelerated when running GPUmotif on NVIDIA graphics cards. As a result, energy consumption can also be greatly reduced when running motif analysis using GPUmotif. The GPUmotif program is freely available at http://sourceforge.net/projects/gpumotif

Lack of conservation of bacterial type promoters in plastids of Streptophyta

<p>Abstract</p> <p/> <p>We demonstrate the scarcity of conserved bacterial-type promoters in plastids of Streptophyta and report widely conserved promoters only for genes <it>psaA, psbA, psbB, psbE, rbcL</it>. Among the reasonable explanations are: evolutionary changes of sigma subunit paralogs and phage-type RNA polymerases possibly entailing the loss of corresponding nuclear genes, <it>de novo </it>emergence of the promoters, their loss together with plastome genes; functional substitution of the promoter boxes by transcription activation factor binding sites.</p> <p>Reviewers</p> <p>This article was reviewed by Dr. Arcady Mushegian, and by Dr. Alexander Bolshoy and Dr. Yuri Wolf (both nominated by Dr. Purificación López-García).</p

On the detection and refinement of transcription factor binding sites using ChIP-Seq data

Coupling chromatin immunoprecipitation (ChIP) with recently developed massively parallel sequencing technologies has enabled genome-wide detection of protein–DNA interactions with unprecedented sensitivity and specificity. This new technology, ChIP-Seq, presents opportunities for in-depth analysis of transcription regulation. In this study, we explore the value of using ChIP-Seq data to better detect and refine transcription factor binding sites (TFBS). We introduce a novel computational algorithm named Hybrid Motif Sampler (HMS), specifically designed for TFBS motif discovery in ChIP-Seq data. We propose a Bayesian model that incorporates sequencing depth information to aid motif identification. Our model also allows intra-motif dependency to describe more accurately the underlying motif pattern. Our algorithm combines stochastic sampling and deterministic ‘greedy’ search steps into a novel hybrid iterative scheme. This combination accelerates the computation process. Simulation studies demonstrate favorable performance of HMS compared to other existing methods. When applying HMS to real ChIP-Seq datasets, we find that (i) the accuracy of existing TFBS motif patterns can be significantly improved; and (ii) there is significant intra-motif dependency inside all the TFBS motifs we tested; modeling these dependencies further improves the accuracy of these TFBS motif patterns. These findings may offer new biological insights into the mechanisms of transcription factor regulation

A Monte Carlo-based framework enhances the discovery and interpretation of regulatory sequence motifs

Abstract Background Discovery of functionally significant short, statistically overrepresented subsequence patterns (motifs) in a set of sequences is a challenging problem in bioinformatics. Oftentimes, not all sequences in the set contain a motif. These non-motif-containing sequences complicate the algorithmic discovery of motifs. Filtering the non-motif-containing sequences from the larger set of sequences while simultaneously determining the identity of the motif is, therefore, desirable and a non-trivial problem in motif discovery research. Results We describe MotifCatcher, a framework that extends the sensitivity of existing motif-finding tools by employing random sampling to effectively remove non-motif-containing sequences from the motif search. We developed two implementations of our algorithm; each built around a commonly used motif-finding tool, and applied our algorithm to three diverse chromatin immunoprecipitation (ChIP) data sets. In each case, the motif finder with the MotifCatcher extension demonstrated improved sensitivity over the motif finder alone. Our approach organizes candidate functionally significant discovered motifs into a tree, which allowed us to make additional insights. In all cases, we were able to support our findings with experimental work from the literature. Conclusions Our framework demonstrates that additional processing at the sequence entry level can significantly improve the performance of existing motif-finding tools. For each biological data set tested, we were able to propose novel biological hypotheses supported by experimental work from the literature. Specifically, in Escherichia coli, we suggested binding site motifs for 6 non-traditional LexA protein binding sites; in Saccharomyces cerevisiae, we hypothesize 2 disparate mechanisms for novel binding sites of the Cse4p protein; and in Halobacterium sp. NRC-1, we discoverd subtle differences in a general transcription factor (GTF) binding site motif across several data sets. We suggest that small differences in our discovered motif could confer specificity for one or more homologous GTF proteins. We offer a free implementation of the MotifCatcher software package at http://www.bme.ucdavis.edu/facciotti/resources_data/software/ .http://deepblue.lib.umich.edu/bitstream/2027.42/112965/1/12859_2012_Article_5570.pd

Population Genetics of Populus trichocarpa for Targeted Breeding as a Biofuel Crop

Populus trichocarpa (poplar) is a woody species native to the western U.S. and Canada. As a fast-growing crop, it has been under investigation by the Department of Energy as a resource for liquid biofuel production. Having recently expanded the collection of poplar whole-genome sequences so that it spans the entire natural species range, we have the novel opportunity to study adaptive responses across this range. This work starts with an initial proof of concept study in a well-studied portion of the species range that has complete whole-genome sequences and RNA expression. The completeness of these data allow robust validation of the methods used. Having demonstrated our methods, we expand our study to include the unstudied portion\u27s of poplar\u27s range. Using these data, we map nine novel genomic loci as regulators of climactic response. This comes with the implication that poplar\u27s response to abiotic stressors are not a genome-wide phenomenon, but restricted to a small number of key regulators. This novel finding has massive implications on the feasibility of engineering poplar for climate resiliency, and enhanced biomass production. The final study leverages this understanding to investigate the predictive capacity that resides in the genome for these climate and geo-spatial traits. Initial results highlight promising avenues for future investigations using our association mapping targets. We are also able to use this prediction architecture to identify mislabeled genotypes, and subsequently correct their labels

Bounded Search for de Novo Identification of Degenerate Cis-Regulatory Elements

The identification of statistically overrepresented sequences in the upstream regions of coregulated genes should theoretically permit the identification of potential cis-regulatory elements. However, in practice many cis-regulatory elements are highly degenerate, precluding the use of an exhaustive word-counting strategy for their identification. While numerous methods exist for inferring base distributions using a position weight matrix, recent studies suggest that the independence assumptions inherent in the model, as well as the inability to reach a global optimum, limit this approach

Context-based RNA-seq mapping

In recent years, the sequencing of RNA (RNA-seq) using next generation sequencing (NGS) technology has become a powerful tool for analyzing the transcriptomic state of a cell. Modern NGS platforms allow for performing RNA-seq experiments in a few days, resulting in millions of short sequencing reads. A crucial step in analyzing RNA-seq data generally is determining the transcriptomic origin of the sequencing reads (= read mapping). In principal, read mapping is a sequence alignment problem, in which the short sequencing reads (30 - 500 nucleotides) are aligned to much larger reference sequences such as the human genome (3 billion nucleotides). In this thesis, we present ContextMap, an RNA-seq mapping approach that evaluates the context of the sequencing reads for determining the most likely origin of every read. The context of a sequencing read is defined by all other reads aligned to the same genomic region. The ContextMap project started with a proof of concept study, in which we showed that our approach is able to improve already existing read mapping results provided by other mapping programs. Subsequently, we developed a standalone version of ContextMap. This implementation no longer relied on mapping results of other programs, but determined initial alignments itself using a modification of the Bowtie short read alignment program. However, the original ContextMap implementation had several drawbacks. In particular, it was not able to predict reads spanning over more than two exons and to detect insertions or deletions (indels). Furthermore, ContextMap depended on a modification of a specific Bowtie version. Thus, it could neither benefit of Bowtie updates nor of novel developments (e.g. improved running times) in the area of short read alignment software. For addressing these problems, we developed ContextMap 2, an extension of the original ContextMap algorithm. The key features of ContextMap 2 are the context-based resolution of ambiguous read alignments and the accurate detection of reads crossing an arbitrary number of exon-exon junctions or containing indels. Furthermore, a plug-in interface is provided that allows for the easy integration of alternative short read alignment programs (e.g. Bowtie 2 or BWA) into the mapping workflow. The performance of ContextMap 2 was evaluated on real-life as well as synthetic data and compared to other state-of-the-art mapping programs. We found that ContextMap 2 had very low rates of misplaced reads and incorrectly predicted junctions or indels. Additionally, recall values were as high as for the top competing methods. Moreover, the runtime of ContextMap 2 was at least two fold lower than for the best competitors. In addition to the mapping of sequencing reads to a single reference, the ContextMap approach allows the investigation of several potential read sources (e.g. the human host and infecting pathogens) in parallel. Thus, ContextMap can be applied to mine for infections or contaminations or to map data from meta-transcriptomic studies. Furthermore, we developed methods based on mapping-derived statistics that allow to assess confidence of mappings to identified species and to detect false positive hits. ContextMap was evaluated on three real-life data sets and results were compared to metagenomics tools. Here, we showed that ContextMap can successfully identify the species contained in a sample. Moreover, in contrast to most other metagenomics approaches, ContextMap also provides read mapping results to individual species. As a consequence, read mapping results determined by ContextMap can be used to study the gene expression of all species contained in a sample at the same time. Thus, ContextMap might be applied in clinical studies, in which the influence of infecting agents on host organisms is investigated. The methods presented in this thesis allow for an accurate and fast mapping of RNA-seq data. As the amount of available sequencing data increases constantly, these methods will likely become an important part of many RNA-seq data analyses and thus contribute valuably to research in the field of transcriptomics