PnpProbs: A better multiple sequence alignment tool by better handling of guide trees

published_or_final_versio

GRASPx: efficient homolog-search of short peptide metagenome database through simultaneous alignment and assembly

Background Metagenomics is a cultivation-independent approach that enables the study of the genomic composition of microbes present in an environment. Metagenomic samples are routinely sequenced using next-generation sequencing technologies that generate short nucleotide reads. Proteins identified from these reads are mostly of partial length. On the other hand, de novo assembly of a large metagenomic dataset is computationally demanding and the assembled contigs are often fragmented, resulting in the identification of protein sequences that are also of partial length and incomplete. Annotation of an incomplete protein sequence often proceeds by identifying its homologs in a database of reference sequences. Identifying the homologs of incomplete sequences is a challenge and can result in substandard annotation of proteins from metagenomic datasets. To address this problem, we recently developed a homology detection algorithm named GRASP (Guided Reference-based Assembly of Short Peptides) that identifies the homologs of a given reference protein sequence in a database of short peptide metagenomic sequences. GRASP was developed to implement a simultaneous alignment and assembly algorithm for annotation of short peptides identified on metagenomic reads. The program achieves significantly improved recall rate at the cost of computational efficiency. In this article, we adopted three techniques to speed up the original version of GRASP, including the pre-construction of extension links, local assembly of individual seeds, and the implementation of query-level parallelism. Results The resulting new program, GRASPx, achieves >30X speedup compared to its predecessor GRASP. At the same time, we show that the performance of GRASPx is consistent with that of GRASP, and that both of them significantly outperform other popular homology-search tools including the BLAST and FASTA suites. GRASPx was also applied to a human saliva metagenome dataset and shows superior performance for both recall and precision rates. Conclusions In this article we present GRASPx, a fast and accurate homology-search program implementing a simultaneous alignment and assembly framework. GRASPx can be used for more comprehensive and accurate annotation of short peptides. GRASPx is freely available at http://graspx.sourceforge.net/. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1119-1) contains supplementary material, which is available to authorized users

Correcting Gene Trees by Leaf Insertions: Complexity and Approximation

Abstract Gene tree correction has recently gained interest in phylogenomics, as it gives insights in understanding the evolution of gene families. Following some recent approaches based on leaf edit operations, we consider a variant of the problem where a gene tree is corrected by inserting leaves with labels in a multiset M. We show that the problem of deciding whether a gene tree can be corrected by inserting leaves with labels in M is NP-complete. Then, we consider an optimization variant of the problem that asks for the correction of a gene tree with leaves labeled by a multiset M ′ , with M ′ ⊇ M , having minimum size. For this optimization variant of the problem, we present a factor 2 approximation algorithm

Computational Methods for Sequencing and Analysis of Heterogeneous RNA Populations

Next-generation sequencing (NGS) and mass spectrometry technologies bring unprecedented throughput, scalability and speed, facilitating the studies of biological systems. These technologies allow to sequence and analyze heterogeneous RNA populations rather than single sequences. In particular, they provide the opportunity to implement massive viral surveillance and transcriptome quantification. However, in order to fully exploit the capabilities of NGS technology we need to develop computational methods able to analyze billions of reads for assembly and characterization of sampled RNA populations. In this work we present novel computational methods for cost- and time-effective analysis of sequencing data from viral and RNA samples. In particular, we describe: i) computational methods for transcriptome reconstruction and quantification; ii) method for mass spectrometry data analysis; iii) combinatorial pooling method; iv) computational methods for analysis of intra-host viral populations

Efficient Algorithms for Prokaryotic Whole Genome Assembly and Finishing

De-novo genome assembly from DNA fragments is primarily based on sequence overlap information. In addition, mate-pair reads or paired-end reads provide linking information for joining gaps and bridging repeat regions. Genome assemblers in general assemble long contiguous sequences (contigs) using both overlapping reads and linked reads until the assembly runs into an ambiguous repeat region. These contigs are further bridged into scaffolds using linked read information. However, errors can be made in both phases of assembly due to high error threshold of overlap acceptance and linking based on too few mate reads. Identical as well as similar repeat regions can often cause errors in overlap and mate-pair evidence. In addition, the problem of setting the correct threshold to minimize errors and optimize assembly of reads is not trivial and often requires a time-consuming trial and error process to obtain optimal results. The typical trial-and-error with multiple assembler, which can be computationally intensive, and is very inefficient, especially when users must learn how to use a wide variety of assemblers, many of which may be serial requiring long execution time and will not return usable or accurate results. Further, we show that the comparison of assembly results may not provide the users with a clear winner under all circumstances. Therefore, we propose a novel scaffolding tool, Correlative Algorithm for Repeat Placement (CARP), capable of joining short low error contigs using mate pair reads, computationally resolved repeat structures and synteny with one or more reference organisms. The CARP tool requires a set of repeat sequences such as insertion sequences (IS) that can be found computationally found without assembling the genome. Development of methods to identify such repeating regions directly from raw sequence reads or draft genomes led to the development of the ISQuest software package. ISQuest identifies bacterial ISs and their sequence elements—inverted and direct repeats—in raw read data or contigs using flexible search parameters. ISQuest is capable of finding ISs in hundreds of partially assembled genomes within hours; making it a valuable high-throughput tool for a global search of IS and repeat elements. The CARP tool matches very low error contigs with strong overlap using the ambiguous partial repeat sequence at the ends of the contig annotated using the repeat sequences discovered using ISQuest. These matches are verified by synteny with genomes of one or more reference organisms. We show that the CARP tool can be used to verify low mate pair evidence regions, independently find new joins and significantly reduce the number of scaffolds. Finally, we are demonstrate a novel viewer that presents to the user the computationally derived joins along with the evidence used to make the joins. The viewer allows the user to independently assess their confidence in the joins made by the finishing tools and make an informed decision of whether to invest the resources necessary to confirm a particular portion of the assembly. Further, we allow users to manually record join evidence, re-order contigs, and track the assembly finishing process