scholarly journals A Massively Parallel Sequence Similarity Search for Metagenomic Sequencing Data

2017 ◽  
Vol 18 (10) ◽  
pp. 2124 ◽  
Author(s):  
Masanori Kakuta ◽  
Shuji Suzuki ◽  
Kazuki Izawa ◽  
Takashi Ishida ◽  
Yutaka Akiyama
Keyword(s):  
Similarity Search ◽  
Sequence Similarity ◽  
Massively Parallel ◽  
Metagenomic Sequencing ◽  
Sequence Similarity Search ◽  
Sequencing Data

Filtering Redundancies For Sequence Similarity Search Programs

2005 ◽  
Vol 22 (4) ◽  
pp. 487-492
Author(s):  
Hubert Cantalloube ◽  
Jacques Chomilier ◽  
Sylvain Chiusa ◽  
Mathieu Lonquety ◽  
Jean-Louis Spadoni ◽  
...  
Keyword(s):  
Similarity Search ◽  
Sequence Similarity ◽  
Sequence Similarity Search

scholarly journals Rfam 14: expanded coverage of metagenomic, viral and microRNA families

2020 ◽  
Vol 49 (D1) ◽  
pp. D192-D200 ◽  
Author(s):  
Ioanna Kalvari ◽  
Eric P Nawrocki ◽  
Nancy Ontiveros-Palacios ◽  
Joanna Argasinska ◽  
Kevin Lamkiewicz ◽  
...  
Keyword(s):  
Similarity Search ◽  
Sequence Similarity ◽  
Sequence Similarity Search ◽  
Covariance Model ◽  
Rna Sequences ◽  
Multiple Sequence ◽  
The Family ◽  
Recent Developments ◽  
Community Contribution ◽  
Website Features

Abstract Rfam is a database of RNA families where each of the 3444 families is represented by a multiple sequence alignment of known RNA sequences and a covariance model that can be used to search for additional members of the family. Recent developments have involved expert collaborations to improve the quality and coverage of Rfam data, focusing on microRNAs, viral and bacterial RNAs. We have completed the first phase of synchronising microRNA families in Rfam and miRBase, creating 356 new Rfam families and updating 40. We established a procedure for comprehensive annotation of viral RNA families starting with Flavivirus and Coronaviridae RNAs. We have also increased the coverage of bacterial and metagenome-based RNA families from the ZWD database. These developments have enabled a significant growth of the database, with the addition of 759 new families in Rfam 14. To facilitate further community contribution to Rfam, expert users are now able to build and submit new families using the newly developed Rfam Cloud family curation system. New Rfam website features include a new sequence similarity search powered by RNAcentral, as well as search and visualisation of families with pseudoknots. Rfam is freely available at https://rfam.org.

scholarly journals Minimally-overlapping words for sequence similarity search

2020 ◽  
Author(s):  
Martin C Frith ◽  
Laurent Noé ◽  
Gregory Kucherov
Keyword(s):  
Similarity Search ◽  
Sequence Similarity ◽  
Random Sequence ◽  
Supplementary Information ◽  
Sequence Similarity Search ◽  
Supplementary Data ◽  
Huge Data ◽  
Open Questions ◽  
Seeding Method ◽  
Genetic Sequences

Abstract Motivation Analysis of genetic sequences is usually based on finding similar parts of sequences, e.g. DNA reads and/or genomes. For big data, this is typically done via “seeds”: simple similarities (e.g. exact matches) that can be found quickly. For huge data, sparse seeding is useful, where we only consider seeds at a subset of positions in a sequence. Results Here we study a simple sparse-seeding method: using seeds at positions of certain “words” (e.g. ac, at, gc, or gt). Sensitivity is maximized by using words with minimal overlaps. That is because, in a random sequence, minimally-overlapping words are anti-clumped. We provide evidence that this is often superior to acclaimed “minimizer” sparse-seeding methods. Our approach can be unified with design of inexact (spaced and subset) seeds, further boosting sensitivity. Thus, we present a promising approach to sequence similarity search, with open questions on how to optimize it. Availability and Implementation Software to design and test minimally-overlapping words is freely available at https://gitlab.com/mcfrith/noverlap. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Powerful Sequence Similarity Search Methods and In-Depth Manual Analyses Can Identify Remote Homologs in Many Apparently "Orphan" Viral Proteins

2013 ◽  
Vol 88 (1) ◽  
pp. 10-20 ◽  
Author(s):  
D. B. Kuchibhatla ◽  
W. A. Sherman ◽  
B. Y. W. Chung ◽  
S. Cook ◽  
G. Schneider ◽  
...  
Keyword(s):  
Similarity Search ◽  
Sequence Similarity ◽  
Viral Proteins ◽  
Sequence Similarity Search ◽  
Search Methods ◽  
Remote Homologs

scholarly journals Improved microbial community characterization of 16S rRNA via metagenome hybridization capture enrichment

2020 ◽  
Author(s):  
Megan Sarah Beaudry ◽  
Jincheng Wang ◽  
Troy Kieran ◽  
Jesse Thomas ◽  
Natalia Juliana Bayona-Vasquez ◽  
...  
Keyword(s):  
16S Rrna ◽  
Sequence Similarity ◽  
Rrna Gene ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
16S Rrna Sequence ◽  
Shotgun Metagenomics ◽  
Reference Databases ◽  
Rrna Sequences ◽  
16S Rrna Sequences

Environmental microbial diversity is often investigated from a molecular perspective using 16S ribosomal RNA (rRNA) gene amplicons and shotgun metagenomics. While amplicon methods are fast, low-cost, and have curated reference databases, they can suffer from amplification bias and are limited in genomic scope. In contrast, shotgun metagenomic methods sample more genomic regions with fewer sequence acquisition biases. However, shotgun metagenomic sequencing is much more expensive (even with moderate sequencing depth) and computationally challenging. Here, we develop a set of 16S rRNA sequence capture baits that offer a potential middle ground with the advantages from both approaches for investigating microbial communities. These baits cover the diversity of all 16S rRNA sequences available in the Greengenes (v. 13.5) database, with no sequence having < 80% sequence similarity to at least one bait for all segments of 16S. The use of our baits provide comparable results to 16S amplicon libraries and shotgun metagenomic libraries when assigning taxonomic units from 16S sequences within the metagenomic reads. We demonstrate that 16S rRNA capture baits can be used on a range of microbial samples (i.e., mock communities and rodent fecal samples) to increase the proportion of 16S rRNA sequences (average >400-fold) and decrease analysis time to obtain consistent community assessments. Furthermore, our study reveals that bioinformatic methods used to analyze sequencing data may have a greater influence on estimates of community composition than library preparation method used, likely in part to the extent and curation of the reference databases considered.

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