GenSeed-HMM: A Tool for Progressive Assembly Using Profile HMMs as Seeds and its Application in Alpavirinae Viral Discovery from Metagenomic Data

Abstract

This work reports the development of GenSeed-HMM, a program that implements seed-driven progressive assembly, an approach to reconstruct specific sequences from unassembled data, starting from short nucleotide or protein seed sequences or profile Hidden Markov Models (HMM). The program can use any one of a number of sequence assemblers. Assembly is performed in multiple steps and relatively few reads are used in each cycle, consequently the program demands low computational resources. As a proof-of-concept and to demonstrate the power of HMM-driven progressive assemblies, GenSeed-HMM was applied to metagenomic datasets in the search for diverse ssDNA bacteriophages from the recently described Alpavirinae subfamily. Profile HMMs were built using Alpavirinae-specific regions from multiple sequence alignments (MSA) using either the viral protein 1 (VP1; major capsid protein) or VP4 (genome replication initiation protein). These profile HMMs were used by GenSeed-HMM (running Newbler assembler) as seeds to reconstruct viral genomes from sequencing datasets of human fecal samples. All contigs obtained were annotated and taxonomically classified using similarity searches and phylogenetic analyses. The most specific profile HMM seed enabled the reconstruction of 45 partial or complete Alpavirinae genomic sequences. A comparison with conventional (global) assembly of the same original dataset, using Newbler in a standalone execution, revealed that GenSeed-HMM outperformed global genomic assembly in several metrics employed. This approach is capable of detecting organisms that have not been used in the construction of the profile HMM, which opens up the possibility of diagnosing novel viruses, without previous specific information, constituting a de novo diagnosis. Additional applications include, but are not limited to, the specific assembly of extrachromosomal elements such as plastid and mitochondrial genomes from metagenomic data. Profile HMM seeds can also be used to reconstruct specific protein coding genes for gene diversity studies, and to determine all possible gene variants present in a metagenomic sample. Such surveys could be useful to detect the emergence of drug-resistance variants in sensitive environments such as hospitals and animal production facilities, where antibiotics are regularly used. Finally, GenSeed-HMM can be used as an adjunct for gap closure on assembly finishing projects, by using multiple contig ends as anchored seeds.

Virome Diversity Correlates with Intestinal Microbiome Diversity in Adult Monozygotic Twins

“…Microviridae HMM-profiles (Alves et al, 2016) https://doi.org/10.17632/zb9swhpv3y.1 MVP database (Gao et al, 2018) http://mvp.medgenius.info…”

“…Finally, the HMM-crAsslike profile was built using hmmbuild from the HMMER package v.3.1b2 (http://hmmer.org/) (Eddy, 1998). For the Microviridae case, all HMM-profiles for the viral protein 1 (VP1) developed by Alves et al (2016) were adopted.…”

PathoLive – Real-time pathogen identification from metagenomic Illumina datasets

“…Thus, it allows for an unbiased diagnostic analysis. There is a variety of tool able to address NGS-based pathogen related questions with different focuses: either aiming to discover yet unknown genomes [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] or to detect known species in a sample [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40]. Among both groups, there are different underlying algorithms, the main distinction running between alignment-based [15-17, 19, 23, 25, 26, 28-31, 33, 35-37, 39, 40] and alignment-free methods [6,9,12,21,32,38].…”

“…Among both groups, there are different underlying algorithms, the main distinction running between alignment-based [15-17, 19, 23, 25, 26, 28-31, 33, 35-37, 39, 40] and alignment-free methods [6,9,12,21,32,38]. Many tools of course combine both approaches [5,7,8,10,11,13,14,18,20,22,27,34]. While being faster in most cases, alignment-free methods are limited to the detection of sequences, whereas alignment-based methods potentially allow for a more detailed characterization of genomes.…”

Virtual Genome Walking: Generating gene models for the salamanderAmbystoma mexicanum

“…These can include GC bias, highly repetitive sequences, sheer scale or even financial limitation. Recently, Keinath and colleagues generated 20x short read Illumina coverage of similar to previous technologies designed to fill gaps within genome assemblies (such as Gapfiller and IMAGE) (Boetzer and Pirovano 2012;Tsai et al 2010), assemble flanking data (GenSeed, Tracembler) (Alves et al 2016;Dong et al 2007) and assemble exons (Lamichhaney et al 2012). A similar protocol has been independently proposed by Aluome and colleagues, although no pipeline was made available (Aluome et al 2016).…”