Faculty Opinions recommendation of De novo assembly of human genomes with massively parallel short read sequencing.

Author(s):  
Tony Long
2009 ◽  
Vol 20 (2) ◽  
pp. 265-272 ◽  
Author(s):  
R. Li ◽  
H. Zhu ◽  
J. Ruan ◽  
W. Qian ◽  
X. Fang ◽  
...  

BMC Genomics ◽  
2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Nam V. Hoang ◽  
Agnelo Furtado ◽  
Patrick J. Mason ◽  
Annelie Marquardt ◽  
Lakshmi Kasirajan ◽  
...  

Data in Brief ◽  
2021 ◽  
Vol 34 ◽  
pp. 106674
Author(s):  
Stafny DSouza ◽  
Koushik Ponnanna ◽  
Amruthavalli Chokkanna ◽  
Nallur Ramachandra

2019 ◽  
Author(s):  
Nicolas C. Rochette ◽  
Angel G. Rivera-Colón ◽  
Julian M. Catchen

AbstractFor half a century population genetics studies have put type II restriction endonucleases to work. Now, coupled with massively-parallel, short-read sequencing, the family of RAD protocols that wields these enzymes has generated vast genetic knowledge from the natural world. Here we describe the first software capable of using paired-end sequencing to derive short contigs from de novo RAD data natively. Stacks version 2 employs a de Bruijn graph assembler to build contigs from paired-end reads and overlap those contigs with the corresponding single-end loci. The new architecture allows all the individuals in a meta population to be considered at the same time as each RAD locus is processed. This enables a Bayesian genotype caller to provide precise SNPs, and a robust algorithm to phase those SNPs into long haplotypes – generating RAD loci that are 400-800bp in length. To prove its recall and precision, we test the software with simulated data and compare reference-aligned and de novo analyses of three empirical datasets. We show that the latest version of Stacks is highly accurate and outperforms other software in assembling and genotyping paired-end de novo datasets.


Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 5201-5201
Author(s):  
Ute Fischer ◽  
Layal Yasin ◽  
Julia Täubner ◽  
Triantafyllia Brozou ◽  
Arndt Borkhardt

Germline mutations account for a substantial proportion of childhood cancer and may critically affect disease characteristics, therapy efficacy, severity of treatment side effects and patient outcome. To date, only 8-10% of childhood cancer cases can be explained by germline mutations identified in known cancer predisposing genes. This is in part due to the technical limitation of next generation short read sequencing, which detects single nucleotide variants, small deletions/insertions or simple copy number variations, but is not a reliable tool to identify larger structural variations (SVs, >500 bp) which are frequent in the human genome and may impact on disease predisposition. Using whole genome optical mapping (WGOM) we aimed at identification of de novo and inherited germline SVs in a cohort of patients with clinically suspected cancer predisposition but without informative findings in short read sequencing analyses. After informed consent we performed family trio based short read (2x 100 bp) whole exome sequencing (WES) on a HiSeq2500 (Illumina) and collected clinical and demographic data for a cohort of >100 families with children affected by cancer who were treated in our hospital. About 25% of the patients either (1) had a family history indicative of cancer susceptibility, or (2) had accompanying clinical findings (e.g. developmental delay, congenital anomalies) or (3) experienced excessive toxicity during chemotherapy. From this subgroup we selected four patients with acute lymphoblastic leukemia whose sequencing data and routine genetic workup were not informative of a known cancer predisposing syndrome and employed family trio-based next generation WGOM on a Saphyr instrument equipped with Access software (Bionano Genomics) to identify genomic SVs. To this end, we extracted and labeled high molecular weight DNA molecules at specific hexamer sequence motifs (average distance: 5 kb) using a DNA methyltransferase-based direct labeling reaction. Imaging was carried out on single-molecule level and each sample genome was de novo assembled from molecule data. Consensus genome maps were clustered into two alleles and diploid assemblies created. Genomes of patients were compared to parental genomes and the GRCh38 reference genome. SVs were inferred from de novo assemblies and genome comparisons with respect to quality scores, overall molecule coverage, fraction of molecules displaying the SV event, and chimeric DNA fragment mapping. Specific SV calls were compared to a set of > 160 human control samples (provided by Bionano Genomics) to filter against common SVs and potential artifacts. Filtered SVs were annotated using structural variant and gene databases. Employing WGOM we analyzed DNA molecules 300.000 bp long on average and achieved genomic coverage ranging from 90-132x corresponding to 330-480 Gbp. For instance, for one patient, we obtained 1751 insertions, 624 deletions, 77 inversions, 21 duplications, 1 intra- and 2 inter-chromosomal translocations before filtering. The majority of these events (78%) were inherited from both parents. 20% were inherited from either father or mother and 2% were generated de novo. As the family history of this patient was inconspicuous for tumor diseases, we removed all inherited events and filtered against common variants. This resulted in only two candidate de novo lesions: a heterozygous 129,495 bp deletion framed by inversions (chr9: 66,156,733-66,622,623) in a gene-less region and a heterozygous inverted 352,667 bp duplication (chr22: 15,522,454-15.875,120) that spanned the genes OR11H, POTEH, POTEH-AS1, LINC01297, DUXAP8, and BMS1P22. Of these genes DUXAP8 is an oncogenic non-coding RNA of the homeobox gene family that has been associated with increased tumor growth and poorer prognosis in a wide variety of somatic cancers. It functions as a regulator of transcription by binding to key components of the developmental regulator epigenetic polycomb repressive complex 2 and may thus account for additional presentations of the child (dwarfism, accelerated skeletal age, linguistic developmental delay, morphological traits). Our results indicate that WGOM is a useful technology to identify candidate SVs in children predisposed to cancer and developmental syndromes. Several candidates are currently being tested and the results will be presented. Disclosures No relevant conflicts of interest to declare.


2016 ◽  
Author(s):  
Li Fang ◽  
Jiang Hu ◽  
Depeng Wang ◽  
Kai Wang

AbstractBackgroundStructural variants (SVs) in human genomes are implicated in a variety of human diseases. Long-read sequencing delivers much longer read lengths than short-read sequencing and may greatly improve SV detection. However, due to the relatively high cost of long-read sequencing, it is unclear what coverage is needed and how to optimally use the aligners and SV callers.ResultsIn this study, we developed NextSV, a meta-caller to perform SV calling from low coverage long-read sequencing data. NextSV integrates three aligners and three SV callers and generates two integrated call sets (sensitive/stringent) for different analysis purposes. We evaluated SV calling performance of NextSV under different PacBio coverages on two personal genomes, NA12878 and HX1. Our results showed that, compared with running any single SV caller, NextSV stringent call set had higher precision and balanced accuracy (F1 score) while NextSV sensitive call set had a higher recall. At 10X coverage, the recall of NextSV sensitive call set was 93.5% to 94.1% for deletions and 87.9% to 93.2% for insertions, indicating that ~10X coverage might be an optimal coverage to use in practice, considering the balance between the sequencing costs and the recall rates. We further evaluated the Mendelian errors on an Ashkenazi Jewish trio dataset.ConclusionsOur results provide useful guidelines for SV detection from low coverage whole-genome PacBio data and we expect that NextSV will facilitate the analysis of SVs on long-read sequencing data.


2011 ◽  
Vol 29 (8) ◽  
pp. 723-730 ◽  
Author(s):  
Yingrui Li ◽  
Hancheng Zheng ◽  
Ruibang Luo ◽  
Honglong Wu ◽  
Hongmei Zhu ◽  
...  

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