Genomic insights into the host specific adaptation of the Pneumocystis genus

AbstractPneumocystis jirovecii, the fungal agent of human Pneumocystis pneumonia, is closely related to macaque Pneumocystis. Little is known about other Pneumocystis species in distantly related mammals, none of which are capable of establishing infection in humans. The molecular basis of host specificity in Pneumocystis remains unknown as experiments are limited due to an inability to culture any species in vitro. To explore Pneumocystis evolutionary adaptations, we have sequenced the genomes of species infecting macaques, rabbits, dogs and rats and compared them to available genomes of species infecting humans, mice and rats. Complete whole genome sequence data enables analysis and robust phylogeny, identification of important genetic features of the host adaptation, and estimation of speciation timing relative to the rise of their mammalian hosts. Our data reveals insights into the evolution of P. jirovecii, the sole member of the genus able to infect humans.

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Genomic insights into the host specific adaptation of the Pneumocystis genus and emergence of the human pathogen Pneumocystis jirovecii

10.1101/2020.07.29.227421 ◽

2020 ◽

Author(s):

Ousmane H. Cissé ◽

Liang Ma ◽

John P. Dekker ◽

Pavel P. Khil ◽

Jung-Ho Youn ◽

...

Keyword(s):

Molecular Basis ◽

Sequence Data ◽

Host Adaptation ◽

Pneumocystis Jirovecii ◽

Whole Genome Sequence ◽

Whole Genome ◽

Human Pathogen ◽

Specific Adaptation ◽

Genetic Features

AbstractPneumocystis jirovecii, the fungal agent of human Pneumocystis pneumonia, is closely related to macaque Pneumocystis. Little is known about other Pneumocystis species in distantly related mammals, none of which are capable of establishing infection in humans. The molecular basis of host specificity in Pneumocystis remains unknown as experiments are limited due to an inability to culture any species in vitro. To explore Pneumocystis evolutionary adaptations, we have sequenced the genomes of species infecting macaques, rabbits, dogs and rats and compared them to available genomes of species infecting humans, mice and rats. Complete whole genome sequence data enables analysis and robust phylogeny, identification of important genetic features of the host adaptation, and estimation of speciation timing relative to the rise of their mammalian hosts. Our data reveals novel insights into the evolution of P. jirovecii, the sole member of the genus able to infect humans.

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Fungal Genomic Resources for Strain Identification and Diversity Analysis of 1900 Fungal Species

Journal of Fungi ◽

10.3390/jof7040288 ◽

2021 ◽

Vol 7 (4) ◽

pp. 288

Author(s):

Mir Asif Iquebal ◽

Sarika Jaiswal ◽

Vineet Kumar Mishra ◽

Rahul Singh Jasrotia ◽

Ulavappa B. Angadi ◽

...

Keyword(s):

Genome Sequence ◽

In Silico ◽

Sequence Data ◽

Fungal Species ◽

Genomic Research ◽

Whole Genome Sequence ◽

Strain Identification ◽

Diversity Analysis ◽

Whole Genome

Identification and diversity analysis of fungi is greatly challenging. Though internal transcribed spacer (ITS), region-based DNA fingerprinting works as a “gold standard” for most of the fungal species group, it cannot differentiate between all the groups and cryptic species. Therefore, it is of paramount importance to find an alternative approach for strain differentiation. Availability of whole genome sequence data of nearly 2000 fungal species are a promising solution to such requirement. We present whole genome sequence-based world’s largest microsatellite database, FungSatDB having >19M loci obtained from >1900 fungal species/strains using >4000 assemblies across globe. Genotyping efficacy of FungSatDB has been evaluated by both in-silico and in-vitro PCR. By in silico PCR, 66 strains of 8 countries representing four continents were successfully differentiated. Genotyping efficacy was also evaluated by in vitro PCR in four fungal species. This approach overcomes limitation of ITS in species, strain signature, and diversity analysis. It can accelerate fungal genomic research endeavors in agriculture, industrial, and environmental management.

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Faculty Opinions recommendation of Optimal algorithms for haplotype assembly from whole-genome sequence data.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.13339986.14707085 ◽

2011 ◽

Author(s):

Alejandro Schaffer

Keyword(s):

Genome Sequence ◽

Sequence Data ◽

Whole Genome Sequence ◽

Whole Genome ◽

Optimal Algorithms ◽

Genome Sequence Data ◽

Haplotype Assembly

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TIGER: inferring DNA replication timing from whole-genome sequence data

Bioinformatics ◽

10.1093/bioinformatics/btab166 ◽

2021 ◽

Cited By ~ 1

Author(s):

Amnon Koren ◽

Dashiell J Massey ◽

Alexa N Bracci

Keyword(s):

Dna Replication ◽

Genome Sequence ◽

Genomic Dna ◽

Sequence Data ◽

Replication Timing ◽

Whole Genome Sequence ◽

Supplementary Information ◽

Whole Genome ◽

Genome Sequence Data ◽

Dna Replication Timing

Abstract Motivation Genomic DNA replicates according to a reproducible spatiotemporal program, with some loci replicating early in S phase while others replicate late. Despite being a central cellular process, DNA replication timing studies have been limited in scale due to technical challenges. Results We present TIGER (Timing Inferred from Genome Replication), a computational approach for extracting DNA replication timing information from whole genome sequence data obtained from proliferating cell samples. The presence of replicating cells in a biological specimen leads to non-uniform representation of genomic DNA that depends on the timing of replication of different genomic loci. Replication dynamics can hence be observed in genome sequence data by analyzing DNA copy number along chromosomes while accounting for other sources of sequence coverage variation. TIGER is applicable to any species with a contiguous genome assembly and rivals the quality of experimental measurements of DNA replication timing. It provides a straightforward approach for measuring replication timing and can readily be applied at scale. Availability and Implementation TIGER is available at https://github.com/TheKorenLab/TIGER. Supplementary information Supplementary data are available at Bioinformatics online

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Whole genome sequence data of Bacillus australimaris strain B28A, isolated from Marine Water in India

Data in Brief ◽

10.1016/j.dib.2021.107240 ◽

2021 ◽

pp. 107240

Author(s):

Wael Ali Mohammed Hadi ◽

Boby T Edwin ◽

A Jayakumaran Nair

Keyword(s):

Genome Sequence ◽

Sequence Data ◽

Marine Water ◽

Whole Genome Sequence ◽

Whole Genome ◽

Genome Sequence Data

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Whole genome sequence data of Mycobacterium tuberculosis XDR strain, isolated from patient in Kazakhstan

Data in Brief ◽

10.1016/j.dib.2020.106416 ◽

2020 ◽

Vol 33 ◽

pp. 106416

Author(s):

Asset Daniyarov ◽

Askhat Molkenov ◽

Saule Rakhimova ◽

Ainur Akhmetova ◽

Zhannur Nurkina ◽

...

Keyword(s):

Mycobacterium Tuberculosis ◽

Genome Sequence ◽

Sequence Data ◽

Whole Genome Sequence ◽

Whole Genome ◽

Genome Sequence Data

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Elucidating the genetic basis of an oligogenic birth defect using whole genome sequence data in a non-model organism, Bubalus bubalis

Scientific Reports ◽

10.1038/srep39719 ◽

2017 ◽

Vol 7 (1) ◽

Cited By ~ 10

Author(s):

Lynsey K. Whitacre ◽

Jesse L. Hoff ◽

Robert D. Schnabel ◽

Sara Albarella ◽

Francesca Ciotola ◽

...

Keyword(s):

Genome Sequence ◽

Birth Defect ◽

Genetic Basis ◽

Sequence Data ◽

Model Organism ◽

Bubalus Bubalis ◽

Whole Genome Sequence ◽

Whole Genome ◽

Genome Sequence Data

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46 Footprints of Selection in Angus and Hanwoo Beef Cattle Using Imputed Whole Genome Sequence Data

Journal of Animal Science ◽

10.1093/jas/skab235.042 ◽

2021 ◽

Vol 99 (Supplement_3) ◽

pp. 25-25

Author(s):

Muhammad Yasir Nawaz ◽

Rodrigo Pelicioni Savegnago ◽

Cedric Gondro

Keyword(s):

Beef Cattle ◽

Genome Sequence ◽

Sequence Data ◽

Whole Genome Sequence ◽

Fixation Index ◽

Whole Genome ◽

Extended Haplotype Homozygosity ◽

Extended Haplotype ◽

Genome Sequence Data ◽

Genomic Regions

Abstract In this study, we detected genome wide footprints of selection in Hanwoo and Angus beef cattle using different allele frequency and haplotype-based methods based on imputed whole genome sequence data. Our dataset included 13,202 Angus and 10,437 Hanwoo animals with 10,057,633 and 13,241,550 imputed SNPs, respectively. A subset of data with 6,873,624 common SNPs between the two populations was used to estimate signatures of selection parameters, both within (runs of homozygosity and extended haplotype homozygosity) and between (allele fixation index, extended haplotype homozygosity) the breeds in order to infer evidence of selection. We observed that correlations between various measures of selection ranged between 0.01 to 0.42. Assuming these parameters were complementary to each other, we combined them into a composite selection signal to identify regions under selection in both beef breeds. The composite signal was based on the average of fractional ranks of individual selection measures for every SNP. We identified some selection signatures that were common between the breeds while others were independent. We also observed that more genomic regions were selected in Angus as compared to Hanwoo. Candidate genes within significant genomic regions may help explain mechanisms of adaptation, domestication history and loci for important traits in Angus and Hanwoo cattle. In the future, we will use the top SNPs under selection for genomic prediction of carcass traits in both breeds.

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