Patterns of microsatellite distribution across eukaryotic genomes

Srivastava, Surabhi; Avvaru, Akshay Kumar; Sowpati, Divya Tej; Mishra, Rakesh K.

doi:10.1186/s12864-019-5516-5

Cited by 91 publications

(111 citation statements)

References 32 publications

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“…The 55 complete genomes of Coronaviridae families were retrieved on March 23, 2020 (See Supplementary Material 1, Sheet 2 for more details, we used only RefSeq Nucleotides with complete annotations) and were scanned in search of SSRs using a Python package, PERF [ 2 ]. A minimum length of SSRs was chosen to be 12 nt [ 24 , 34 ] which represents at least two complete repeating units of a 6-mer motif (hexamer). We used all theoretically possible 501 unique classes of SSRs as described in a study [ 34 , 35 ] to identify their presence/absence in Coronaviridae genomes by using the following command: “PERF -isequence.fasta -a -o sequence_perf_default.tsv” .…”

Section: Methodsmentioning

confidence: 99%

“…A minimum length of SSRs was chosen to be 12 nt [ 24 , 34 ] which represents at least two complete repeating units of a 6-mer motif (hexamer). We used all theoretically possible 501 unique classes of SSRs as described in a study [ 34 , 35 ] to identify their presence/absence in Coronaviridae genomes by using the following command: “PERF -isequence.fasta -a -o sequence_perf_default.tsv” . The interactive.…”

Section: Methodsmentioning

confidence: 99%

“…We also checked for variation of repeat class abundance with respect to repeat class lengths using python script from Ref. [ 34 ].…”

Section: Methodsmentioning

confidence: 99%

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Deciphering the SSR incidences across viral members of Coronaviridae family

Satyam¹,

Jha

Kar

et al. 2020

Chemico-Biological Interactions

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Presence of Simple Sequence Repeats (SSRs), both in genic and intergenic regions, have been widely studied in eukaryotes, prokaryotes, and viruses. In the current study, we undertook a survey to analyze the frequency and distribution of microsatellites or SSRs in multiple genomes of Coronaviridae members. We successfully identified 919 SSRs with length≥12 bp across 55 reference genomes majority of which (838 S SRs) were found abundant in genic regions. The in-silico analysis further identified the preferential abundance of hexameric SSRs than any other size-based motif class. Our analysis shows that the genome size and GC content of the genome had a weak influence on SSR frequency and density. However, we find a positive correlation of SSRs GC content with genomic GC content. We also report relatively low abundances of all theoretically possible 501 repeat motif classes in all the genomes of Coronaviridae . The majority of SSRs were AT-rich. Overall, we see an underrepresentation of SSRs across the genomes of Coronaviridae . Besides, our integrative study highlights the presence of SSRs in ORF1ab (nsp3, nsp4, nsp5A_3CLpro and nsp5B_3CLpro, nsp6, nsp10, nsp12, nsp13, & nsp15 domains), S, ORF3a, ORF7a, N & 3′ UTR regions of SARS-CoV-2 and harbours multiple mutations (3′UTR and ORF1ab SSRs serving as major mutational hotspots). This indicates the genic SSRs are under selection pressure against mutations that might alter the reading frame and at the same time responsible for rapid protein evolution. Our preliminary results indicate the significance of the limited repertoire of SSRs in the genomes of Coronaviridae.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Deciphering the SSR incidences across viral members of Coronaviridae family

Satyam¹,

Jha

Kar

et al. 2020

Chemico-Biological Interactions

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“…We set L max as the longest length of micro-satellites. The lengths of micro-satellites are generally less than 50kbps [27]. Thus, L max is set to be 50kbps.…”

Section: Sb-readmentioning

confidence: 99%

Accurately estimating the length distributions of genomic micro-satellites by tumor purity deconvolution

et al. 2020

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Background: Genomic micro-satellites are the genomic regions that consist of short and repetitive DNA motifs. Estimating the length distribution and state of a micro-satellite region is an important computational step in cancer sequencing data pipelines, which is suggested to facilitate the downstream analysis and clinical decision supporting. Although several state-of-the-art approaches have been proposed to identify micro-satellite instability (MSI) events, they are limited in dealing with regions longer than one read length. Moreover, based on our best knowledge, all of these approaches imply a hypothesis that the tumor purity of the sequenced samples is sufficiently high, which is inconsistent with the reality, leading the inferred length distribution to dilute the data signal and introducing the false positive errors. Results: In this article, we proposed a computational approach, named ELMSI, which detected MSI events based on the next generation sequencing technology. ELMSI can estimate the specific length distributions and states of micro-satellite regions from a mixed tumor sample paired with a control one. It first estimated the purity of the tumor sample based on the read counts of the filtered SNVs loci. Then, the algorithm identified the length distributions and the states of short micro-satellites by adding the Maximum Likelihood Estimation (MLE) step to the existing algorithm. After that, ELMSI continued to infer the length distributions of long micro-satellites by incorporating a simplified Expectation Maximization (EM) algorithm with central limit theorem, and then used statistical tests to output the states of these micro-satellites. Based on our experimental results, ELMSI was able to handle micro-satellites with lengths ranging from shorter than one read length to 10kbps. Conclusions: To verify the reliability of our algorithm, we first compared the ability of classifying the shorter (Continued on next page)

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“…Further studies showed the genomic distribution of SSRs is nonrandom. SSRs in genes may influence gene transcription or translation and gene activity [6,23], and recent studies showed a higher abundance of SSRs in response to environmental stress [24]. The polymorphism levels and potential functions of SSRs differ among the 5 untranslated region (5 UTR), the 3 UTR and coding sequences (CDs) are different; SSRs in 5 UTR may affect transcription or translation, SSRs in CDS may inactivate or activate genes, or truncate proteins, and SSRs in 3 UTR may cause silencing or slippage [25].…”

Section: Introductionmentioning

confidence: 99%

Characteristics of Microsatellites Mined from Transcriptome Data and the Development of Novel Markers in Paeonia lactiflora

Wan

Zhang

Hong³

et al. 2020

Genes

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The insufficient number of available simple sequence repeats (SSRs) inhibits genetic research on and molecular breeding of Paeonia lactiflora, a flowering crop with great economic value. The objective of this study was to develop SSRs for P. lactiflora with Illumina RNA sequencing and assess the role of SSRs in gene regulation. The results showed that dinucleotides with AG/CT repeats were the most abundant type of repeat motif in P. lactiflora and were preferentially distributed in untranslated regions. Significant differences in SSR size were observed among motif types and locations. A large number of unigenes containing SSRs participated in catalytic activity, metabolic processes and cellular processes, and 28.16% of all transcription factors and 21.74% of hub genes for inflorescence stem straightness were found to contain SSRs. Successful amplification was achieved with 89.05% of 960 pairs of SSR primers, 55.83% of which were polymorphic, and most of the 46 tested primers had a high level of transferability to the genus Paeonia. Principal component and cluster dendrogram analyses produced results consistent with known genealogical relationships. This study provides a set of SSRs with abundant information for future accession identification, marker-trait association and molecular assisted breeding in P. lactiflora.

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Patterns of microsatellite distribution across eukaryotic genomes

Cited by 91 publications

References 32 publications

Deciphering the SSR incidences across viral members of Coronaviridae family

Deciphering the SSR incidences across viral members of Coronaviridae family

Accurately estimating the length distributions of genomic micro-satellites by tumor purity deconvolution

Characteristics of Microsatellites Mined from Transcriptome Data and the Development of Novel Markers in Paeonia lactiflora

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