Identifying statistical dependence in genomic sequences via mutual information estimates

Questions of understanding and quantifying the representation and amount of information in organisms have become a central part of biological research, as they potentially hold the key to fundamental advances. In this paper, we demonstrate the use of information-theoretic tools for the task of identifying segments of biomolecules (DNA or RNA) that are statistically correlated. We develop a precise and reliable methodology, based on the notion of mutual information, for finding and extracting statistical as well as structural dependencies. A simple threshold function is defined, and its use in quantifying the level of significance of dependencies between biological segments is explored. These tools are used in two specific applications. First, for the identification of correlations between different parts of the maize zmSRp32 gene. There, we find significant dependencies between the 5' untranslated region in zmSRp32 and its alternatively spliced exons. This observation may indicate the presence of as-yet unknown alternative splicing mechanisms or structural scaffolds. Second, using data from the FBI's Combined DNA Index System (CODIS), we demonstrate that our approach is particularly well suited for the problem of discovering short tandem repeats, an application of importance in genetic profiling.Comment: Preliminary version. Final version in EURASIP Journal on Bioinformatics and Systems Biology. See http://www.hindawi.com/journals/bsb

Identifying Structural Variation in Haploid Microbial Genomes from Short-Read Resequencing Data Using Breseq

Mutations that alter chromosomal structure play critical roles in evolution and disease, including in the origin of new lifestyles and pathogenic traits in microbes. Large-scale rearrangements in genomes are often mediated by recombination events involving new or existing copies of mobile genetic elements, recently duplicated genes, or other repetitive sequences. Most current software programs for predicting structural variation from short-read DNA resequencing data are intended primarily for use on human genomes. They typically disregard information in reads mapping to repeat sequences, and significant post-processing and manual examination of their output is often required to rule out false-positive predictions and precisely describe mutational events. Results: We have implemented an algorithm for identifying structural variation from DNA resequencing data as part of the breseq computational pipeline for predicting mutations in haploid microbial genomes. Our method evaluates the support for new sequence junctions present in a clonal sample from split-read alignments to a reference genome, including matches to repeat sequences. Then, it uses a statistical model of read coverage evenness to accept or reject these predictions. Finally, breseq combines predictions of new junctions and deleted chromosomal regions to output biologically relevant descriptions of mutations and their effects on genes. We demonstrate the performance of breseq on simulated Escherichia coli genomes with deletions generating unique breakpoint sequences, new insertions of mobile genetic elements, and deletions mediated by mobile elements. Then, we reanalyze data from an E. coli K-12 mutation accumulation evolution experiment in which structural variation was not previously identified. Transposon insertions and large-scale chromosomal changes detected by breseq account for similar to 25% of spontaneous mutations in this strain. In all cases, we find that breseq is able to reliably predict structural variation with modest read-depth coverage of the reference genome (>40-fold). Conclusions: Using breseq to predict structural variation should be useful for studies of microbial epidemiology, experimental evolution, synthetic biology, and genetics when a reference genome for a closely related strain is available. In these cases, breseq can discover mutations that may be responsible for important or unintended changes in genomes that might otherwise go undetected.U.S. National Institutes of Health R00-GM087550U.S. National Science Foundation (NSF) DEB-0515729NSF BEACON Center for the Study of Evolution in Action DBI-0939454Cancer Prevention & Research Institute of Texas (CPRIT) RP130124University of Texas at Austin startup fundsUniversity of Texas at AustinCPRIT Cancer Research TraineeshipMolecular Bioscience

Transcription Factor-DNA Binding Via Machine Learning Ensembles

We present ensemble methods in a machine learning (ML) framework combining predictions from five known motif/binding site exploration algorithms. For a given TF the ensemble starts with position weight matrices (PWM's) for the motif, collected from the component algorithms. Using dimension reduction, we identify significant PWM-based subspaces for analysis. Within each subspace a machine classifier is built for identifying the TF's gene (promoter) targets (Problem 1). These PWM-based subspaces form an ML-based sequence analysis tool. Problem 2 (finding binding motifs) is solved by agglomerating k-mer (string) feature PWM-based subspaces that stand out in identifying gene targets. We approach Problem 3 (binding sites) with a novel machine learning approach that uses promoter string features and ML importance scores in a classification algorithm locating binding sites across the genome. For target gene identification this method improves performance (measured by the F1 score) by about 10 percentage points over the (a) motif scanning method and (b) the coexpression-based association method. Top motif outperformed 5 component algorithms as well as two other common algorithms (BEST and DEME). For identifying individual binding sites on a benchmark cross species database (Tompa et al., 2005) we match the best performer without much human intervention. It also improved the performance on mammalian TFs. The ensemble can integrate orthogonal information from different weak learners (potentially using entirely different types of features) into a machine learner that can perform consistently better for more TFs. The TF gene target identification component (problem 1 above) is useful in constructing a transcriptional regulatory network from known TF-target associations. The ensemble is easily extendable to include more tools as well as future PWM-based information.Comment: 33 page

Complete Sequences of Organelle Genomes from the Medicinal Plant Rhazya Stricta (Apocynaceae) and Contrasting Patterns of Mitochondrial Genome Evolution Across Asterids

Rhazya stricta is native to arid regions in South Asia and the Middle East and is used extensively in folk medicine to treat a wide range of diseases. In addition to generating genomic resources for this medicinally important plant, analyses of the complete plastid and mitochondrial genomes and a nuclear transcriptome from Rhazya provide insights into inter-compartmental transfers between genomes and the patterns of evolution among eight asterid mitochondrial genomes. Results: The 154,841 bp plastid genome is highly conserved with gene content and order identical to the ancestral organization of angiosperms. The 548,608 bp mitochondrial genome exhibits a number of phenomena including the presence of recombinogenic repeats that generate a multipartite organization, transferred DNA from the plastid and nuclear genomes, and bidirectional DNA transfers between the mitochondrion and the nucleus. The mitochondrial genes sdh3 and rps14 have been transferred to the nucleus and have acquired targeting presequences. In the case of rps14, two copies are present in the nucleus; only one has a mitochondrial targeting presequence and may be functional. Phylogenetic analyses of both nuclear and mitochondrial copies of rps14 across angiosperms suggests Rhazya has experienced a single transfer of this gene to the nucleus, followed by a duplication event. Furthermore, the phylogenetic distribution of gene losses and the high level of sequence divergence in targeting presequences suggest multiple, independent transfers of both sdh3 and rps14 across asterids. Comparative analyses of mitochondrial genomes of eight sequenced asterids indicates a complicated evolutionary history in this large angiosperm clade with considerable diversity in genome organization and size, repeat, gene and intron content, and amount of foreign DNA from the plastid and nuclear genomes. Conclusions: Organelle genomes of Rhazya stricta provide valuable information for improving the understanding of mitochondrial genome evolution among angiosperms. The genomic data have enabled a rigorous examination of the gene transfer events. Rhazya is unique among the eight sequenced asterids in the types of events that have shaped the evolution of its mitochondrial genome. Furthermore, the organelle genomes of R. stricta provide valuable genomic resources for utilizing this important medicinal plant in biotechnology applications.King Abdulaziz UniversityIntegrative Biolog

rMotifGen: random motif generator for DNA and protein sequences

<p>Abstract</p> <p>Background</p> <p>Detection of short, subtle conserved motif regions within a set of related DNA or amino acid sequences can lead to discoveries about important regulatory domains such as transcription factor and DNA binding sites as well as conserved protein domains. In order to help assess motif detection algorithms on motifs with varying properties and levels of conservation, we have developed a computational tool, rMotifGen, with the sole purpose of generating a number of random DNA or protein sequences containing short sequence motifs. Each motif consensus can be user-defined, randomly generated, or created from a position-specific scoring matrix (PSSM). Insertions and mutations within these motifs are created according to user-defined parameters and substitution matrices. The resulting sequences can be helpful in mutational simulations and in testing the limits of motif detection algorithms.</p> <p>Results</p> <p>Two implementations of rMotifGen have been created, one providing a graphical user interface (GUI) for random motif construction, and the other serving as a command line interface. The second implementation has the added advantages of platform independence and being able to be called in a batch mode. rMotifGen was used to construct sample sets of sequences containing DNA motifs and amino acid motifs that were then tested against the Gibbs sampler and MEME packages.</p> <p>Conclusion</p> <p>rMotifGen provides an efficient and convenient method for creating random DNA or amino acid sequences with a variable number of motifs, where the instance of each motif can be incorporated using a position-specific scoring matrix (PSSM) or by creating an instance mutated from its corresponding consensus using an evolutionary model based on substitution matrices. rMotifGen is freely available at: <url>http://bioinformatics.louisville.edu/brg/rMotifGen/</url>.</p

Rebooting the human mitochondrial phylogeny: an automated and scalable methodology with expert knowledge

<p>Abstract</p> <p>Background</p> <p>Mitochondrial DNA is an ideal source of information to conduct evolutionary and phylogenetic studies due to its extraordinary properties and abundance. Many insights can be gained from these, including but not limited to screening genetic variation to identify potentially deleterious mutations. However, such advances require efficient solutions to very difficult computational problems, a need that is hampered by the very plenty of data that confers strength to the analysis.</p> <p>Results</p> <p>We develop a systematic, automated methodology to overcome these difficulties, building from readily available, public sequence databases to high-quality alignments and phylogenetic trees. Within each stage in an autonomous workflow, outputs are carefully evaluated and outlier detection rules defined to integrate expert knowledge and automated curation, hence avoiding the manual bottleneck found in past approaches to the problem. Using these techniques, we have performed exhaustive updates to the human mitochondrial phylogeny, illustrating the power and computational scalability of our approach, and we have conducted some initial analyses on the resulting phylogenies.</p> <p>Conclusions</p> <p>The problem at hand demands careful definition of inputs and adequate algorithmic treatment for its solutions to be realistic and useful. It is possible to define formal rules to address the former requirement by refining inputs directly and through their combination as outputs, and the latter are also of help to ascertain the performance of chosen algorithms. Rules can exploit known or inferred properties of datasets to simplify inputs through partitioning, therefore cutting computational costs and affording work on rapidly growing, otherwise intractable datasets. Although expert guidance may be necessary to assist the learning process, low-risk results can be fully automated and have proved themselves convenient and valuable.</p

HMM-FRAME: accurate protein domain classification for metagenomic sequences containing frameshift errors

<p>Abstract</p> <p>Background</p> <p>Protein domain classification is an important step in metagenomic annotation. The state-of-the-art method for protein domain classification is profile HMM-based alignment. However, the relatively high rates of insertions and deletions in homopolymer regions of pyrosequencing reads create frameshifts, causing conventional profile HMM alignment tools to generate alignments with marginal scores. This makes error-containing gene fragments unclassifiable with conventional tools. Thus, there is a need for an accurate domain classification tool that can detect and correct sequencing errors.</p> <p>Results</p> <p>We introduce HMM-FRAME, a protein domain classification tool based on an augmented Viterbi algorithm that can incorporate error models from different sequencing platforms. HMM-FRAME corrects sequencing errors and classifies putative gene fragments into domain families. It achieved high error detection sensitivity and specificity in a data set with annotated errors. We applied HMM-FRAME in Targeted Metagenomics and a published metagenomic data set. The results showed that our tool can correct frameshifts in error-containing sequences, generate much longer alignments with significantly smaller E-values, and classify more sequences into their native families.</p> <p>Conclusions</p> <p>HMM-FRAME provides a complementary protein domain classification tool to conventional profile HMM-based methods for data sets containing frameshifts. Its current implementation is best used for small-scale metagenomic data sets. The source code of HMM-FRAME can be downloaded at <url>http://www.cse.msu.edu/~zhangy72/hmmframe/</url> and at <url>https://sourceforge.net/projects/hmm-frame/</url>.</p