INFORMAZIONI SU QUESTO EPISODIO
Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.08.10.245308v1?rss=1
Authors: Du, X., Li, L., Liang, F., Liu, S., Zhang, W., Sun, S., Sun, Y., Fan, F., Wang, L., Liang, X., Qiu, W., Fan, G., Wang, O., Yang, W., Zhang, J., Xiao, Y., Wang, Y., Wang, D., Qu, S., Chen, F., Huang, J.
Abstract:
The importance of structural variants (SVs) on phenotypes and human diseases is now recognized. Although a variety of SV detection platforms and strategies that vary in sensitivity and specificity have been developed, few benchmarking procedures are available to confidently assess their performances in biological and clinical research. To facilitate the validation and application of those approaches, our work established an Asian reference material comprising identified benchmark regions and high-confidence SV calls. We established a high-confidence SV callset with 8,938 SVs in an EBV immortalized B lymphocyte line, by integrating four alignment-based SV callers [from 109x PacBio continuous long read (CLR), 22x PacBio circular consensus sequencing (CCS) reads, 104x Oxford Nanopore long reads, and 114x optical mapping platform (Bionano)] and one de novo assembly-based SV caller using CCS reads. A total of 544 randomly selected SVs were validated by PCR and Sanger sequencing, proofing the robustness of our SV calls. Combining trio-binning based haplotype assemblies, we established an SV benchmark for identification of false negatives and false positives by constructing the continuous high confident regions (CHCRs), which cover 1.46Gb and 6,882 SVs supported by at least one diploid haplotype assembly. Establishing high-confidence SV calls for a benchmark sample that has been characterized by multiple technologies provides a valuable resource for investigating SVs in human biology, disease, and clinical diagnosis.
Copy rights belong to original authors. Visit the link for more info
http://biorxiv.org/cgi/content/short/2020.08.10.245308v1?rss=1
Authors: Du, X., Li, L., Liang, F., Liu, S., Zhang, W., Sun, S., Sun, Y., Fan, F., Wang, L., Liang, X., Qiu, W., Fan, G., Wang, O., Yang, W., Zhang, J., Xiao, Y., Wang, Y., Wang, D., Qu, S., Chen, F., Huang, J.
Abstract:
The importance of structural variants (SVs) on phenotypes and human diseases is now recognized. Although a variety of SV detection platforms and strategies that vary in sensitivity and specificity have been developed, few benchmarking procedures are available to confidently assess their performances in biological and clinical research. To facilitate the validation and application of those approaches, our work established an Asian reference material comprising identified benchmark regions and high-confidence SV calls. We established a high-confidence SV callset with 8,938 SVs in an EBV immortalized B lymphocyte line, by integrating four alignment-based SV callers [from 109x PacBio continuous long read (CLR), 22x PacBio circular consensus sequencing (CCS) reads, 104x Oxford Nanopore long reads, and 114x optical mapping platform (Bionano)] and one de novo assembly-based SV caller using CCS reads. A total of 544 randomly selected SVs were validated by PCR and Sanger sequencing, proofing the robustness of our SV calls. Combining trio-binning based haplotype assemblies, we established an SV benchmark for identification of false negatives and false positives by constructing the continuous high confident regions (CHCRs), which cover 1.46Gb and 6,882 SVs supported by at least one diploid haplotype assembly. Establishing high-confidence SV calls for a benchmark sample that has been characterized by multiple technologies provides a valuable resource for investigating SVs in human biology, disease, and clinical diagnosis.
Copy rights belong to original authors. Visit the link for more info
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Stati Uniti
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Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.08.12.248526v1?rss=1
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Abstract:
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Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.08.12.247528v1?rss=1
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Abstract:
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Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.08.10.245258v1?rss=1
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