scholarly journals NGS-Integrator: An efficient tool for combining multiple NGS data tracks using minimum Bayes’ factors

BMC Genomics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Bronte Wen ◽  
Hyun Jun Jung ◽  
Lihe Chen ◽  
Fahad Saeed ◽  
Mark A. Knepper
Keyword(s):  
Bayes Factor ◽  
Joint Probability ◽  
Regulatory Domains ◽  
Genome Wide ◽  
Genome Wide Data ◽  
Dna Elements ◽  
Efficient Integration ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data ◽  
True Signal

Abstract Background Next-generation sequencing (NGS) is widely used for genome-wide identification and quantification of DNA elements involved in the regulation of gene transcription. Studies that generate multiple high-throughput NGS datasets require data integration methods for two general tasks: 1) generation of genome-wide data tracks representing an aggregate of multiple replicates of the same experiment; and 2) combination of tracks from different experimental types that provide complementary information regarding the location of genomic features such as enhancers. Results NGS-Integrator is a Java-based command line application, facilitating efficient integration of multiple genome-wide NGS datasets. NGS-Integrator first transforms all input data tracks using the complement of the minimum Bayes’ factor so that all values are expressed in the range [0,1] representing the probability of a true signal given the background noise. Then, NGS-Integrator calculates the joint probability for every genomic position to create an integrated track. We provide examples using real NGS data generated in our laboratory and from the mouse ENCODE database. Conclusions Our results show that NGS-Integrator is both time- and memory-efficient. Our examples show that NGS-Integrator can integrate information to facilitate downstream analyses that identify functional regulatory domains along the genome.

scholarly journals HAPDeNovo: a haplotype-based approach for filtering and phasing de novo mutations in linked read sequencing data

2017 ◽  
Author(s):  
Xin Zhou ◽  
Serafim Batzoglou ◽  
Arend Sidow ◽  
Lu Zhang
Keyword(s):  
False Positive ◽  
De Novo ◽  
False Positives ◽  
Sequencing Data ◽  
De Novo Mutations ◽  
Congenital Diseases ◽  
Genome Wide ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data ◽  
Haplotype Information

AbstractBackgroundDe novo mutations (DNMs) are associated with neurodevelopmental and congenital diseases, and their detection can contribute to understanding disease pathogenicity. However, accurate detection is challenging because of their small number relative to the genome-wide false positives in next generation sequencing (NGS) data. Software such as DeNovoGear and TrioDeNovo have been developed to detect DNMs, but at good sensitivity they still produce many false positive calls.ResultsTo address this challenge, we develop HAPDeNovo, a program that leverages phasing information from linked read sequencing, to remove false positive DNMs from candidate lists generated by DNM-detection tools. Short reads from each phasing block are allocated to each of the two haplotypes followed by generating a haploid genotype for each putative DNM.HAPDeNovo removes variants that are called as heterozygous in one of the haplotypes because they are almost certainly false positives. Our experiments on 10X Chromium linked read sequencing trio data reveal that HAPDeNovo eliminates 80% to 99% of false positives regardless of how large the candidate DNM set is.ConclusionsHAPDeNovo leverages the haplotype information from linked read sequencing to remove spurious false positive DNMs effectively, and it increases accuracy of DNM detection dramatically without sacrificing sensitivity.

scholarly journals HAPHPIPE: Haplotype Reconstruction and Phylodynamics for Deep Sequencing of Intra-Host Viral Populations

2020 ◽  
Author(s):  
Matthew L Bendall ◽  
Keylie M Gibson ◽  
Margaret C Steiner ◽  
Uzma Rentia ◽  
Marcos Pérez-Losada ◽  
...  
Keyword(s):  
Deep Sequencing ◽  
De Novo ◽  
Consensus Sequence ◽  
Haplotype Reconstruction ◽  
Consensus Sequences ◽  
Genome Wide ◽  
Genomic Regions ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data ◽  
Generation Sequencing

Abstract Deep sequencing of viral populations using next generation sequencing (NGS) offers opportunities to understand and investigate evolution, transmission dynamics, and population genetics. Currently, the standard practice for processing NGS data to study viral populations is to summarize all the observed sequences from a sample as a single consensus sequence, thus discarding valuable information about the intra-host viral molecular epidemiology. Furthermore, existing analytical pipelines may only analyze genomic regions involved in drug resistance, thus are not suited for full viral genome analysis. Here we present HAPHPIPE, a HAplotype and PHylodynamics PIPEline for genome-wide assembly of viral consensus sequences and haplotypes. The HAPHPIPE protocol includes modules for quality trimming, error correction, de novo assembly, alignment, and haplotype reconstruction. The resulting consensus sequences, haplotypes, and alignments can be further analyzed using a variety of phylogenetic and population genetic software. HAPHPIPE is designed to provide users with a single pipeline to rapidly analyze sequences from viral populations generated from NGS platforms and provide quality output properly formatted for downstream evolutionary analyses.

scholarly journals PharmVIP: A Web-Based Tool for Pharmacogenomic Variant Analysis and Interpretation

2021 ◽  
Vol 11 (11) ◽  
pp. 1230
Author(s):  
Jittima Piriyapongsa ◽  
Chanathip Sukritha ◽  
Pavita Kaewprommal ◽  
Chalermpong Intarat ◽  
Kwankom Triparn ◽  
...  
Keyword(s):  
Human Leukocyte ◽  
Web Based ◽  
Leukocyte Antigen ◽  
Genome Wide ◽  
Immune Mediated ◽  
Hla Genotypes ◽  
One Stop ◽  
Potential Risks ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data

The increasing availability of next generation sequencing (NGS) for personal genomics could promote pharmacogenomics (PGx) discovery and application. However, current tools for analysis and interpretation of pharmacogenomic variants from NGS data are inadequate, as none offer comprehensive analytic functions in a simple, web-based platform. In addition, no tools exist to analyze human leukocyte antigen (HLA) genes for determining potential risks of immune-mediated adverse drug reaction (IM-ADR). We describe PharmVIP, a web-based PGx tool, for one-stop comprehensive analysis and interpretation of genome-wide variants obtained from NGS platforms. PharmVIP comprises three main interpretation modules covering analyses of pharmacogenes involved in pharmacokinetics, pharmacodynamics and IM-ADR. The Guideline module provides Clinical Pharmacogenetics Implementation Consortium (CPIC) drug guideline recommendations based on the translation of genotypic data in genes having guidelines. The HLA module reports HLA genotypes, potential adverse drug reactions, and the relevant drug guidelines. The Pharmacogenes module is employed for prioritizing variants according to variant effect on gene function. Detailed, customizable reports are provided as exportable files and as an interactive web version. PharmVIP is a new integrated NGS workflow for the PGx community to facilitate discovery and clinical application.

scholarly journals CrowdVariant: a crowdsourcing approach to classify copy number variants

2016 ◽  
Author(s):  
Peyton Greenside ◽  
Justin M. Zook ◽  
Marc Salit ◽  
Ryan Poplin ◽  
Madeleine Cule ◽  
...  
Keyword(s):  
Copy Number ◽  
Reference Sample ◽  
Copy Number Variants ◽  
Substantial Improvement ◽  
Small Scale ◽  
High Confidence ◽  
High Quality ◽  
Genome Wide ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data

AbstractCopy number variants (CNVs) are an important type of genetic variation and play a causal role in many diseases. However, they are also notoriously difficult to identify accurately from next-generation sequencing (NGS) data. For larger CNVs, genotyping arrays provide reasonable benchmark data, but NGS allows us to assay a far larger number of small (< 10kbp) CNVs that are poorly captured by array-based methods. The lack of high quality benchmark callsets of small-scale CNVs has limited our ability to assess and improve CNV calling algorithms for NGS data. To address this issue we developed a crowdsourcing framework, called CrowdVariant, that leverages Google’s high-throughput crowdsourcing platform to create a high confidence set of copy number variants for NA24385 (NIST HG002/RM 8391), an Ashkenazim reference sample developed in partnership with the Genome In A Bottle Consortium. In a pilot study we show that crowdsourced classifications, even from non-experts, can be used to accurately assign copy number status to putative CNV calls and thereby identify a high-quality subset of these calls. We then scale our framework genome-wide to identify 1,781 high confidence CNVs, which multiple lines of evidence suggest are a substantial improvement over existing CNV callsets, and are likely to prove useful in benchmarking and improving CNV calling algorithms. Our crowdsourcing methodology may be a useful guide for other genomics applications.

scholarly journals Genetic analysis of amyotrophic lateral sclerosis identifies contributing pathways and cell types

2021 ◽  
Vol 7 (3) ◽  
pp. eabd9036
Author(s):  
Sara Saez-Atienzar ◽  
Sara Bandres-Ciga ◽  
Rebekah G. Langston ◽  
Jonggeol J. Kim ◽  
Shing Wan Choi ◽  
...  
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Membrane Trafficking ◽  
Molecular Mechanisms ◽  
Cell Types ◽  
Polygenic Risk Score ◽  
Genome Wide ◽  
Genome Wide Data ◽  
Data Driven Approach ◽  
Single Nucleus ◽  
Lateral Sclerosis

Despite the considerable progress in unraveling the genetic causes of amyotrophic lateral sclerosis (ALS), we do not fully understand the molecular mechanisms underlying the disease. We analyzed genome-wide data involving 78,500 individuals using a polygenic risk score approach to identify the biological pathways and cell types involved in ALS. This data-driven approach identified multiple aspects of the biology underlying the disease that resolved into broader themes, namely, neuron projection morphogenesis, membrane trafficking, and signal transduction mediated by ribonucleotides. We also found that genomic risk in ALS maps consistently to GABAergic interneurons and oligodendrocytes, as confirmed in human single-nucleus RNA-seq data. Using two-sample Mendelian randomization, we nominated six differentially expressed genes (ATG16L2, ACSL5, MAP1LC3A, MAPKAPK3, PLXNB2, and SCFD1) within the significant pathways as relevant to ALS. We conclude that the disparate genetic etiologies of this fatal neurological disease converge on a smaller number of final common pathways and cell types.

scholarly journals Patient Derived Xenografts for Genome-Driven Therapy of Osteosarcoma

Cells ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 416
Author(s):  
Lorena Landuzzi ◽  
Maria Cristina Manara ◽  
Pier-Luigi Lollini ◽  
Katia Scotlandi
Keyword(s):  
Clinical Trials ◽  
Tumor Heterogeneity ◽  
Functional Studies ◽  
Ngs Data Analysis ◽  
The Many ◽  
Orthotopic Xenografts ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data ◽  
Generation Sequencing ◽  
Somatic Copy Number Alterations

Osteosarcoma (OS) is a rare malignant primary tumor of mesenchymal origin affecting bone. It is characterized by a complex genotype, mainly due to the high frequency of chromothripsis, which leads to multiple somatic copy number alterations and structural rearrangements. Any effort to design genome-driven therapies must therefore consider such high inter- and intra-tumor heterogeneity. Therefore, many laboratories and international networks are developing and sharing OS patient-derived xenografts (OS PDX) to broaden the availability of models that reproduce OS complex clinical heterogeneity. OS PDXs, and new cell lines derived from PDXs, faithfully preserve tumor heterogeneity, genetic, and epigenetic features and are thus valuable tools for predicting drug responses. Here, we review recent achievements concerning OS PDXs, summarizing the methods used to obtain ectopic and orthotopic xenografts and to fully characterize these models. The availability of OS PDXs across the many international PDX platforms and their possible use in PDX clinical trials are also described. We recommend the coupling of next-generation sequencing (NGS) data analysis with functional studies in OS PDXs, as well as the setup of OS PDX clinical trials and co-clinical trials, to enhance the predictive power of experimental evidence and to accelerate the clinical translation of effective genome-guided therapies for this aggressive disease.

scholarly journals Ancient genomic time transect from the Central Asian Steppe unravels the history of the Scythians

2021 ◽  
Vol 7 (13) ◽  
pp. eabe4414
Author(s):  
Guido Alberto Gnecchi-Ruscone ◽  
Elmira Khussainova ◽  
Nurzhibek Kahbatkyzy ◽  
Lyazzat Musralina ◽  
Maria A. Spyrou ◽  
...  
Keyword(s):  
Bronze Age ◽  
Iron Age ◽  
Gene Pools ◽  
Social Rules ◽  
Eurasian Steppe ◽  
Central Asian ◽  
Genome Wide ◽  
Genome Wide Data ◽  
History Of ◽  
First Millennium

The Scythians were a multitude of horse-warrior nomad cultures dwelling in the Eurasian steppe during the first millennium BCE. Because of the lack of first-hand written records, little is known about the origins and relations among the different cultures. To address these questions, we produced genome-wide data for 111 ancient individuals retrieved from 39 archaeological sites from the first millennia BCE and CE across the Central Asian Steppe. We uncovered major admixture events in the Late Bronze Age forming the genetic substratum for two main Iron Age gene-pools emerging around the Altai and the Urals respectively. Their demise was mirrored by new genetic turnovers, linked to the spread of the eastern nomad empires in the first centuries CE. Compared to the high genetic heterogeneity of the past, the homogenization of the present-day Kazakhs gene pool is notable, likely a result of 400 years of strict exogamous social rules.

scholarly journals Genome diversity in Ukraine

GigaScience ◽  
2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Taras K Oleksyk ◽  
Walter W Wolfsberger ◽  
Alexandra M Weber ◽  
Khrystyna Shchubelka ◽  
Olga T Oleksyk ◽  
...  
Keyword(s):  
Sequence Data ◽  
Copy Number Variations ◽  
Genomic Variation ◽  
High Coverage ◽  
Genome Data ◽  
New Information ◽  
Genome Wide ◽  
Public Data ◽  
Genome Wide Data ◽  
Multiple Samples

Abstract Background The main goal of this collaborative effort is to provide genome-wide data for the previously underrepresented population in Eastern Europe, and to provide cross-validation of the data from genome sequences and genotypes of the same individuals acquired by different technologies. We collected 97 genome-grade DNA samples from consented individuals representing major regions of Ukraine that were consented for public data release. BGISEQ-500 sequence data and genotypes by an Illumina GWAS chip were cross-validated on multiple samples and additionally referenced to 1 sample that has been resequenced by Illumina NovaSeq6000 S4 at high coverage. Results The genome data have been searched for genomic variation represented in this population, and a number of variants have been reported: large structural variants, indels, copy number variations, single-nucletide polymorphisms, and microsatellites. To our knowledge, this study provides the largest to-date survey of genetic variation in Ukraine, creating a public reference resource aiming to provide data for medical research in a large understudied population. Conclusions Our results indicate that the genetic diversity of the Ukrainian population is uniquely shaped by evolutionary and demographic forces and cannot be ignored in future genetic and biomedical studies. These data will contribute a wealth of new information bringing forth a wealth of novel, endemic and medically related alleles.

scholarly journals Initial Upper Palaeolithic humans in Europe had recent Neanderthal ancestry

Nature ◽  
2021 ◽  
Vol 592 (7853) ◽  
pp. 253-257 ◽  
Author(s):  
Mateja Hajdinjak ◽  
Fabrizio Mafessoni ◽  
Laurits Skov ◽  
Benjamin Vernot ◽  
Alexander Hübner ◽  
...  
Keyword(s):  
Family History ◽  
East Asia ◽  
Late Pleistocene ◽  
Modern Human ◽  
Human Migration ◽  
Upper Palaeolithic ◽  
Modern Humans ◽  
Genome Wide ◽  
Genome Wide Data

AbstractModern humans appeared in Europe by at least 45,000 years ago1–5, but the extent of their interactions with Neanderthals, who disappeared by about 40,000 years ago6, and their relationship to the broader expansion of modern humans outside Africa are poorly understood. Here we present genome-wide data from three individuals dated to between 45,930 and 42,580 years ago from Bacho Kiro Cave, Bulgaria1,2. They are the earliest Late Pleistocene modern humans known to have been recovered in Europe so far, and were found in association with an Initial Upper Palaeolithic artefact assemblage. Unlike two previously studied individuals of similar ages from Romania7 and Siberia8 who did not contribute detectably to later populations, these individuals are more closely related to present-day and ancient populations in East Asia and the Americas than to later west Eurasian populations. This indicates that they belonged to a modern human migration into Europe that was not previously known from the genetic record, and provides evidence that there was at least some continuity between the earliest modern humans in Europe and later people in Eurasia. Moreover, we find that all three individuals had Neanderthal ancestors a few generations back in their family history, confirming that the first European modern humans mixed with Neanderthals and suggesting that such mixing could have been common.

scholarly journals HGG-21. GERMLINE MUTATIONS IN MSH2 GENE IN PEDIATRIC PATIENTS WITH CONGENITAL AND SPORADIC GLIOBLASTOMA

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii348-iii348
Author(s):  
Maria Ejmont ◽  
Małgorzata Rydzanicz ◽  
Wiesława Grajkowska ◽  
Marta Perek-Polnik ◽  
Agnieszka Sowińska ◽  
...  
Keyword(s):  
Germline Mutations ◽  
Response To Therapy ◽  
Msh2 Gene ◽  
Illumina Hiseq ◽  
Adult Type ◽  
Targeted Next Generation Sequencing ◽  
Pathogenic Variants ◽  
New Treatment ◽  
Next Generation Sequencing Ngs ◽  
Ngs Data

Abstract INTRODUCTION Glioblastoma (GBM) remains one of the biggest therapeutic challenges in neuro-oncology. In spite of multimodal treatment approaches the prognosis of GBM is extremely poor, median survival is estimated about 12–16 months. Although GBM is one of the most common and malignant primary brain tumors, pediatric glioblastoma, including congenital is a very rare tumor, with an incidence of about 1.1–3.4 per million live births. Moreover, the mode of presentation, behavior, response to therapy and molecular background of pediatric glioblastomas differs from adult type of GBM. Until now, about ten patients with congenital glioblastoma have been described and in none of them germline markers were examined. Here we report two patients with GBM, one with congenital tumor with germline mutations in MSH2 gene. METHODS Targeted Next-Generation Sequencing (NGS) of the probands DNA extracted from leucocytes was performed using the TruSight One sequencing panel on an Illumina HiSeq 1500. Applied gene panel investigated the coding sequence and splice sites of 4813 genes associated with known disease phenotypes. The NGS data were analyzed using an in-house procedure. Identified variants were validated by Sanger sequencing. RESULTS NGS analysis of patients constitutional DNA revealed know, pathogenic variants c.940C&gt;T and c.942 + 3A&gt;T in MSH2 gene (NM_000251.3) associated with MMR-dependent hereditary cancer syndromes. CONCLUSION Molecular analysis are heavily needed for better understanding of pediatric GBM etiology and new treatment modality implementation. Identification of this oncogenic driver may provide insight into the pathogenesis of GBM, including congenital cases. Funded by National Science Centre, Poland (2016/23/B/NZ2/03064 and 2016/21/B/NZ2/01785).

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