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Hrant Hovhannisyan

dblp:258/2516 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2020
0000-0003-2627-8505ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
metagenomics
0.412020
CROSSMAPPER: estimating cross-mapping rates and optimizing experimental design in multi-species sequencing studies · Bioinform. 2020
Bioinformatics and computational biology › biostatistics › statistical bioinformatics
sequencing study design
0.412020
CROSSMAPPER: estimating cross-mapping rates and optimizing experimental design in multi-species sequencing studies · Bioinform. 2020

Methods — techniques the papers use, named apart from their topics

read simulation · 0.4back-mapping · 0.4
YearPublicationVenuePosition
2020 CROSSMAPPER: estimating cross-mapping rates and optimizing experimental design in multi-species sequencing studies
abstract
MOTIVATION: Numerous sequencing studies, including transcriptomics of host-pathogen systems, sequencing of hybrid genomes, xenografts, mixed species systems, metagenomics and meta-transcriptomics, involve samples containing genetic material from divergent organisms. A crucial step in these studies is identifying from which organism each sequencing read originated, and the experimental design should be directed to minimize biases caused by cross-mapping of reads to incorrect source genomes. Additionally, pooling of sufficiently different genetic material into a single sequencing library could significantly reduce experimental costs but requires careful planning and assessment of the impact of cross-mapping. Having these applications in mind we designed Crossmapper, the first to our knowledge tool able to assess cross-mapping prior to sequencing, therefore allowing optimization of experimental design. RESULTS: Using any combination of reference genomes, Crossmapper performs read simulation and back-mapping of those reads to the pool of references, quantifies and reports the cross-mapping rates for each organism. Crossmapper performs these analyses with numerous user-specified parameters, including, among others, read length, read layout, coverage, mapping parameters, genomic or transcriptomic data. Additionally, it outputs the results in highly interactive and publication-ready reports. This allows the user to perform multiple comparisons at once and choose the experimental setup minimizing cross-mapping rates. Moreover, Crossmapper can be used for resource optimization in sequencing facilities by pooling different samples into one sequencing library. AVAILABILITY AND IMPLEMENTATION: Crossmapper is a command line tool implemented in Python 3.6 and available as a conda package, allowing effortless installation. The source code, detailed information and a step-by-step tutorial is available at our GitHub page https://github.com/Gabaldonlab/crossmapper. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Hrant Hovhannisyan, Ahmed Ibrahem Hafez, Carlos Lloréns, Toni Gabaldón
Bioinform.1