Human pol II promoter prediction: time series descriptors and machine learning

Although several in silico promoter prediction methods have been developed to date, they are still limited in predictive performance. The limitations are due to the challenge of selecting appropriate features of promoters that distinguish them from non-promoters and the generalization or predictive ability of the machine-learning algorithms. In this paper we attempt to define a novel approach by using unique descriptors and machine-learning methods for the recognition of eukaryotic polymerase II promoters. In this study, non-linear time series descriptors along with non-linear machine-learning algorithms, such as support vector machine (SVM), are used to discriminate between promoter and non-promoter regions. The basic idea here is to use descriptors that do not depend on the primary DNA sequence and provide a clear distinction between promoter and non-promoter regions. The classification model built on a set of 1000 promoter and 1500 non-promoter sequences, showed a 10-fold cross-validation accuracy of 87% and an independent test set had an accuracy >85% in both promoter and non-promoter identification. This approach correctly identified all 20 experimentally verified promoters of human chromosome 22. The high sensitivity and selectivity indicates that n-mer frequencies along with non-linear time series descriptors, such as Lyapunov component stability and Tsallis entropy, and supervised machine-learning methods, such as SVMs, can be useful in the identification of pol II promoters

The theoretical basis of universal identification systems for bacteria and viruses

It is shown that the presence/absence pattern of 1000 random oligomers of length 12� in a bacterial genome is sufficiently characteristic to readily and unambiguously distinguish any known bacterial genome from any other. Even genomes of extremely closely-related organisms, such as strains of the same species, can be thus distinguished. One evident way to implement this approach in a practical assay is with hybridization arrays. It is envisioned that a single universal array can be readily designed that would allow identification of any bacterium that appears in a database of known patterns. We performed in silico experiments to test this idea. Calculations utilizing 105 publicly-available completely-sequenced microbial genomes allowed us to determine appropriate values of the test oligonucleotide length, n, and the number of probe sequences. Randomly chosen n-mers with a constant G + C content were used to form an in silico array and verify (a) how many n-mers from each genome would hybridize on this chip, and (b) how different the fingerprints of different genomes would be. With the appropriate choice of random oligomer length, the same approach can also be used to identify viral or eukaryotic genomes

MetaCluster 5.0: a two-round binning approach for metagenomic data for low-abundance species in a noisy sample

All proceedings papers are available as open access at: OUP Bioinformatics (http://www.eccb12.org/proceedings-talks)MOTIVATION: Metagenomic binning remains an important topic in metagenomic analysis. Existing unsupervised binning methods for next-generation sequencing (NGS) reads do not perform well on (i) samples with low-abundance species or (ii) samples (even with high abundance) when there are many extremely low-abundance species. These two problems are common for real metagenomic datasets. Binning methods that can solve these problems are desirable. RESULTS: We proposed a two-round binning method (MetaCluster 5.0) that aims at identifying both low-abundance and high-abundance species in the presence of a large amount of noise due to many extremely low-abundance species. In summary, MetaCluster 5.0 uses a filtering strategy to remove noise from the extremely low-abundance species. It separate reads of high-abundance species from those of low-abundance species in two different rounds. To overcome the issue of low coverage for low-abundance species, multiple w values are used to group reads with overlapping w-mers, whereas reads from high-abundance species are grouped with high confidence based on a large w and then binning expands to low-abundance species using a relaxed (shorter) w. Compared to the recent tools, TOSS and MetaCluster 4.0, MetaCluster 5.0 can find more species (especially those with low abundance of say 6x to 10x) and can achieve better sensitivity and specificity using less memory and running time. AVAILABILITY: http://i.cs.hku.hk/alse/MetaCluster/ CONTACT: [email protected]_or_final_versionThe 11th European Conference on Computational Biology (ECCB'12), Basel, Switzerland, 9-12 September 2012. In Bioinformatics, 2012, v. 28 n. 18, p. i356-i36

Abundant Oligonucleotides Common to Most Bacteria

BACKGROUND: Bacteria show a bias in their genomic oligonucleotide composition far beyond that dictated by G+C content. Patterns of over- and underrepresented oligonucleotides carry a phylogenetic signal and are thus diagnostic for individual species. Patterns of short oligomers have been investigated by multiple groups in large numbers of bacteria genomes. However, global distributions of the most highly overrepresented mid-sized oligomers have not been assessed across all prokaryotes to date. We surveyed overrepresented mid-length oligomers across all prokaryotes and normalised for base composition and embedded oligomers using zero and second order Markov models. PRINCIPAL FINDINGS: Here we report a presumably ancient set of oligomers conserved and overrepresented in nearly all branches of prokaryotic life, including Archaea. These oligomers are either adenine rich homopurines with one to three guanine nucleosides, or homopyridimines with one to four cytosine nucleosides. They do not show a consistent preference for coding or non-coding regions or aggregate in any coding frame, implying a role in DNA structure and as polypeptide binding sites. Structural parameters indicate these oligonucleotides to be an extreme and rigid form of B-DNA prone to forming triple stranded helices under common physiological conditions. Moreover, the narrow minor grooves of these structures are recognised by DNA binding and nucleoid associated proteins such as HU. CONCLUSION: Homopurine and homopyrimidine oligomers exhibit distinct and unusual structural features and are present at high copy number in nearly all prokaryotic lineages. This fact suggests a non-neutral role of these oligonucleotides for bacterial genome organization that has been maintained throughout evolution

Multiple Comparative Metagenomics using Multiset k-mer Counting

Background. Large scale metagenomic projects aim to extract biodiversity knowledge between different environmental conditions. Current methods for comparing microbial communities face important limitations. Those based on taxonomical or functional assignation rely on a small subset of the sequences that can be associated to known organisms. On the other hand, de novo methods, that compare the whole sets of sequences, either do not scale up on ambitious metagenomic projects or do not provide precise and exhaustive results. Methods. These limitations motivated the development of a new de novo metagenomic comparative method, called Simka. This method computes a large collection of standard ecological distances by replacing species counts by k-mer counts. Simka scales-up today's metagenomic projects thanks to a new parallel k-mer counting strategy on multiple datasets. Results. Experiments on public Human Microbiome Project datasets demonstrate that Simka captures the essential underlying biological structure. Simka was able to compute in a few hours both qualitative and quantitative ecological distances on hundreds of metagenomic samples (690 samples, 32 billions of reads). We also demonstrate that analyzing metagenomes at the k-mer level is highly correlated with extremely precise de novo comparison techniques which rely on all-versus-all sequences alignment strategy or which are based on taxonomic profiling

MetaCluster-TA: taxonomic annotation for metagenomic data based on assembly-assisted binning

This article is part of the supplement: Selected articles from the Twelfth Asia Pacific Bioinformatics Conference (APBC 2014): GenomicsBackground Taxonomic annotation of reads is an important problem in metagenomic analysis. Existing annotation tools, which rely on the approach of aligning each read to the taxonomic structure, are unable to annotate many reads efficiently and accurately as reads (100 bp) are short and most of them come from unknown genomes. Previous work has suggested assembling the reads to make longer contigs before annotation. More reads/contigs can be annotated as a longer contig (in Kbp) can be aligned to a taxon even if it is from an unknown species as long as it contains a conserved region of that taxon. Unfortunately existing metagenomic assembly tools are not mature enough to produce long enough contigs. Binning tries to group reads/contigs of similar species together. Intuitively, reads in the same group (cluster) should be annotated to the same taxon and these reads altogether should cover a significant portion of the genome alleviating the problem of short contigs if the quality of binning is high. However, no existing work has tried to use binning results to help solve the annotation problem. This work explores this direction. Results In this paper, we describe MetaCluster-TA, an assembly-assisted binning-based annotation tool which relies on an innovative idea of annotating binned reads instead of aligning each read or contig to the taxonomic structure separately. We propose the novel concept of the 'virtual contig' (which can be up to 10 Kb in length) to represent a set of reads and then represent each cluster as a set of 'virtual contigs' (which together can be total up to 1 Mb in length) for annotation. MetaCluster-TA can outperform widely-used MEGAN4 and can annotate (1) more reads since the virtual contigs are much longer; (2) more accurately since each cluster of long virtual contigs contains global information of the sampled genome which tends to be more accurate than short reads or assembled contigs which contain only local information of the genome; and (3) more efficiently since there are much fewer long virtual contigs to align than short reads. MetaCluster-TA outperforms MetaCluster 5.0 as a binning tool since binning itself can be more sensitive and precise given long virtual contigs and the binning results can be improved using the reference taxonomic database. Conclusions MetaCluster-TA can outperform widely-used MEGAN4 and can annotate more reads with higher accuracy and higher efficiency. It also outperforms MetaCluster 5.0 as a binning tool.published_or_final_versio

Informational laws of genome structures

In recent years, the analysis of genomes by means of strings of length k occurring in the genomes, called k-mers, has provided important insights into the basic mechanisms and design principles of genome structures. In the present study, we focus on the proper choice of the value of k for applying information theoretic concepts that express intrinsic aspects of genomes. The value k\u2009=\u2009lg2(n), where n is the genome length, is determined to be the best choice in the definition of some genomic informational indexes that are studied and computed for seventy genomes. These indexes, which are based on information entropies and on suitable comparisons with random genomes, suggest five informational laws, to which all of the considered genomes obey. Moreover, an informational genome complexity measure is proposed, which is a generalized logistic map that balances entropic and anti-entropic components of genomes and is related to their evolutionary dynamics. Finally, applications to computational synthetic biology are briefly outlined

MicroRNA enrichment among short ‘ultraconserved’ sequences in insects

MicroRNAs are short (∼22 nt) regulatory RNA molecules that play key roles in metazoan development and have been implicated in human disease. First discovered in Caenorhabditis elegans, over 2500 microRNAs have been isolated in metazoans and plants; it has been estimated that there may be more than a thousand microRNA genes in the human genome alone. Motivated by the experimental observation of strong conservation of the microRNA let-7 among nearly all metazoans, we developed a novel methodology to characterize the class of such strongly conserved sequences: we identified a non-redundant set of all sequences 20 to 29 bases in length that are shared among three insects: fly, bee and mosquito. Among the few hundred sequences greater than 20 bases in length are close to 40% of the 78 confirmed fly microRNAs, along with other non-coding RNAs and coding sequence

Libra: scalable k-mer-based tool for massive all-vs-all metagenome comparisons

Background Shotgun metagenomics provides powerful insights into microbial community biodiversity and function. Yet, inferences from metagenomic studies are often limited by dataset size and complexity and are restricted by the availability and completeness of existing databases. De novo comparative metagenomics enables the comparison of metagenomes based on their total genetic content. Results We developed a tool called Libra that performs an all-vs-all comparison of metagenomes for precise clustering based on their k-mer content. Libra uses a scalable Hadoop framework for massive metagenome comparisons, Cosine Similarity for calculating the distance using sequence composition and abundance while normalizing for sequencing depth, and a web-based implementation in iMicrobe (http://imicrobe.us) that uses the CyVerse advanced cyberinfrastructure to promote broad use of the tool by the scientific community. Conclusions A comparison of Libra to equivalent tools using both simulated and real metagenomic datasets, ranging from 80 million to 4.2 billion reads, reveals that methods commonly implemented to reduce compute time for large datasets, such as data reduction, read count normalization, and presence/absence distance metrics, greatly diminish the resolution of large-scale comparative analyses. In contrast, Libra uses all of the reads to calculate k-mer abundance in a Hadoop architecture that can scale to any size dataset to enable global-scale analyses and link microbial signatures to biological processes.National Science Foundation [1640775]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]