A fast weak motif-finding algorithm based on community detection in graphs

Computational discovery and modeling of novel gene expression rules encoded in the mRNA

The transcript is populated with numerous overlapping codes that regulate all steps of gene expression. Deciphering these codes is very challenging due to the large number of variables involved, the non-modular nature of the codes, biases and limitations in current experimental approaches, our limited knowledge in gene expression regulation across the tree of life, and other factors. In recent years, it has been shown that computational modeling and algorithms can significantly accelerate the discovery of novel gene expression codes. Here, we briefly summarize the latest developments and different approaches in the field.

Species- and Strain-Specific Adaptation of the HSP70 Super Family in Pathogenic Trypanosomatids

All eukaryotic genomes encode multiple members of the heat shock protein 70 (HSP70) family, which evolved distinctive structural and functional features in response to specific environmental constraints. Phylogenetic analysis of this protein family thus can inform on genetic and molecular mechanisms that drive species-specific environmental adaptation. Here we use the eukaryotic pathogen Leishmania spp. as a model system to investigate the evolution of the HSP70 protein family in an early-branching eukaryote that is prone to gene amplification and adapts to cytotoxic host environments by stress-induced and chaperone-dependent stage differentiation. Combining phylogenetic and comparative analyses of trypanosomatid genomes, draft genome of Paratrypanosoma and recently published genome sequences of 204 L. donovani field isolates, we gained unique insight into the evolutionary dynamics of the Leishmania HSP70 protein family. We provide evidence for (i) significant evolutionary expansion of this protein family in Leishmania through gene amplification and functional specialization of highly conserved canonical HSP70 members, (ii) evolution of trypanosomatid-specific, non-canonical family members that likely gained ATPase-independent functions, and (iii) loss of one atypical HSP70 member in the Trypanosoma genus. Finally, we reveal considerable copy number variation of canonical cytoplasmic HSP70 in highly related L. donovani field isolates, thus identifying this locus as a potential hot spot of environment–genotype interaction. Our data draw a complex picture of the genetic history of HSP70 in trypanosomatids that is driven by the remarkable plasticity of the Leishmania genome to undergo massive intra-chromosomal gene amplification to compensate for the absence of regulated transcriptional control in these parasites.

A Comparative Analysis Between k-Mers and Community Detection-Based Features for the Task of Protein Classification

Machine learning algorithms are widely used to annotate biological sequences. Low-dimensional informative feature vectors can be crucial for the performance of the algorithms. In prior work, we have proposed the use of a community detection approach to construct low dimensional feature sets for nucleotide sequence classification. Our approach used the Hamming distance between short nucleotide subsequences, called k-mers, to construct a network, and subsequently used community detection to identify groups of k -mers that appear frequently in a set of sequences. Whereas this approach worked well for nucleotide sequence classification, it could not be directly used for protein sequences, as the Hamming distance is not a good measure for comparing short protein k-mers. To address this limitation, we extended our prior approach by replacing the Hamming distance with substitution scores. Experimental results in different learning scenarios show that the features generated with the new approach are more informative than k-mers.

MoTeX-II: structured MoTif eXtraction from large-scale datasets

BACKGROUND

Identifying repeated factors that occur in a string of letters or common factors that occur in a set of strings represents an important task in computer science and biology. Such patterns are called motifs, and the process of identifying them is called motif extraction. In biology, motif extraction constitutes a fundamental step in understanding regulation of gene expression. State-of-the-art tools for motif extraction have their own constraints. Most of these tools are only designed for single motif extraction; structured motifs additionally allow for distance intervals between their single motif components. Moreover, motif extraction from large-scale datasets-for instance, large-scale ChIP-Seq datasets-cannot be performed by current tools. Other constraints include high time and/or space complexity for identifying long motifs with higher error thresholds.

RESULTS

In this article, we introduce MoTeX-II, a word-based high-performance computing tool for structured MoTif eXtraction from large-scale datasets. Similar to its predecessor for single motif extraction, it uses state-of-the-art algorithms for solving the fixed-length approximate string matching problem. It produces similar and partially identical results to state-of-the-art tools for structured motif extraction with respect to accuracy as quantified by statistical significance measures. Moreover, we show that it matches or outperforms these tools in terms of runtime efficiency by merging single motif occurrences efficiently. MoTeX-II comes in three flavors: a standard CPU version; an OpenMP-based version; and an MPI-based version. For instance, the MPI-based version of MoTeX-II requires only a couple of hours to process all human genes for structured motif extraction on 1056 processors, while current sequential tools require more than a week for this task. Finally, we show that MoTeX-II is successful in extracting known composite transcription factor binding sites from real datasets.

CONCLUSIONS

Use of MoTeX-II in biological frameworks may enable deriving reliable and important information since real full-length datasets can now be processed with almost any set of input parameters for both single and structured motif extraction in a reasonable amount of time. The open-source code of MoTeX-II is freely available at http://www.inf.kcl.ac.uk/research/projects/motex/.

Practical guidelines for the comprehensive analysis of ChIP-seq data

Mapping the chromosomal locations of transcription factors, nucleosomes, histone modifications, chromatin remodeling enzymes, chaperones, and polymerases is one of the key tasks of modern biology, as evidenced by the Encyclopedia of DNA Elements (ENCODE) Project. To this end, chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is the standard methodology. Mapping such protein-DNA interactions in vivo using ChIP-seq presents multiple challenges not only in sample preparation and sequencing but also for computational analysis. Here, we present step-by-step guidelines for the computational analysis of ChIP-seq data. We address all the major steps in the analysis of ChIP-seq data: sequencing depth selection, quality checking, mapping, data normalization, assessment of reproducibility, peak calling, differential binding analysis, controlling the false discovery rate, peak annotation, visualization, and motif analysis. At each step in our guidelines we discuss some of the software tools most frequently used. We also highlight the challenges and problems associated with each step in ChIP-seq data analysis. We present a concise workflow for the analysis of ChIP-seq data in Figure 1 that complements and expands on the recommendations of the ENCODE and modENCODE projects. Each step in the workflow is described in detail in the following sections.