Generation Sequencing Data Analysis Research Articles

Artificial intelligence for Next generation sequencing data analysis

In the rapidly evolving field of genomics, our capacity to decipher genetic data encoded in DNA has been transformed by Next Generation Sequencing (NGS) technologies. These advanced technologies produce an enormous volume of data, posing substantial challenges in extracting meaningful biological insights. Artificial intelligence (AI) algorithms offer distinctive possibilities to unravel the biological information embedded in such extensive and intricate datasets. This review offers a synopsis of AI classifications and algorithms, elucidating how these techniques can be employed on sequencing data. Subsequently, a selection of the most typical, promising, or illustrative applications of AI on NGS data to tackle unresolved technical or biological issues are showcased.

Virome characterization and identification of a putative parvovirus and poxvirus in bat ectoparasites of Yunnan Province, China

Ectoparasites found on bats are known to contain important microbes. However, the viruses hosted by these obligate parasites are understudied. This has led to the near oversight of the potential role of these ectoparasites in virus maintenance and transmission from bats to other interacting species and the environment. Here, we sampled bat ectoparasites parasitizing a diverse selection of bat species in the families Rhinolophidae, Vespertilionidae, Megadermatidae, Hipposideridae and Pteropodidae in Yunnan Province, China. We show that the ectoparasite prevalence was generally higher in male compared to female bats. Most ectoparasites were found to fall within the Nycteribiidae, Spinturnicidae and Streblidae bat ectoparasite families. We subsequently applied a non-biased sequencing of libraries prepared from the pooled ectoparasites, followed by an in-silico virus-centric analysis of the resultant reads. We show that ectoparasites hosted by the sampled families of bats are found to carry, in addition to a diverse set of phages, vertebrate and insect viruses in the families Aliusviridae, Ascoviridae, Chuviridae, Circoviridae, Flaviviridae, Hepadnaviridae, Hepeviridae, Herpesviridae, Iridoviridae, Marseilleviridae, Nairoviridae, Orthomyxoviridae, Parvoviridae, Poxviridae, Reoviridae, Retroviridae, and Rhabdoviridae. We further report a partial Parvovirus VP1/VP2 gene and partial Poxvirus ubiquitin-like gene predicted by two independent next generation sequencing data analysis pipelines. This study describes the natural virome of bat ectoparasites, providing a platform for understanding the role these ectoparasites play in the maintenance and spread of viruses to other animals.

Two Novel Mutations in LAMC2 Gene in Iranian Families Affected by Junctional Epidermolysis Bullosa.

Junctional epidermolysis bullosa (JEB) is an autosomal recessive skin disorder with defective adhesion of dermal- epidermal within the lamina lucida region of the basement membrane zone. The main characterization of JEB is blistering and fragile skin and mucous membrane. Laminins are noncollagenous part of basement membrane and classified as a family of extracellular matrix glycoprotein. Laminins contain three chains: Laminin α, Laminin β and Laminin γ. LAMC2 (laminin subunit gamma 2) gene encodes γ subunit of laminin and its mutation contributes to JEB. Here, we report a disease-causing nonsense mutation and a large deletion mutation in LAMC2 gene in two families affected by JEB. Whole exome sequencing (WES) was carried out on the mother of patient in family I and the patient himself in family II to detect the underlying mutations. Then, sanger sequencing was performed to confirm the identified mutations. Next generation sequencing (NGS) data analysis of the first family showed a novel, nonsense mutation in LAMC2 gene (LAMC2: NM_005562: exon14:c.C2143T: p.R715X). The heterozygous state of the mutation was confirmed by sanger sequencing in the parents and unaffected brother. In Family II, NGS data had no coverage in the large area of LAMC2 gene. Thus, to confirm the possible deletion sanger sequencing was done and blasting of sequence showed the deleted region of 9.4 kb (exon10-17) in LAMC2 gene. In summary, current study reported a novel disease-causing premature termination codon (PTC) mutation in LAMC2 gene and a large deletion mutation in patients affected by JEB.

Abstract 2280: A comprehensive sample tracking and data processing workflow for next generation sequencing

Abstract The successful application of Next Generation Sequencing (NGS) to drug discovery requires systems to manage and document each step of the sequencing process from sample receipt through data generation and data processing. We combined BenchlingTM, a solution for tracking NGS lab processes, with FONDA (Framework Of Next generation sequencing Data Analysis) an internally developed data processing platform, to support multiple types of NGS data generation and processing. Benchling combines a digital notebook and a laboratory information management system (LIMS). The system documents and automates steps in the NGS process including: sample registration, nucleic acid extraction, library construction, flow cell construction, sequencer sample sheet generation and BCL2FASTQ conversion. This enables wet lab scientists to easily retrieve an appropriate protocol for each sample and sequencing library type. We connected our sequencers to Benchling in order to monitor each sequencing run and to keep track of the quality of NGS data. In addition, it generates “analysis ready sample sheet” (contains project and study information, location of FASTQ, sample species and library type) and uploads it into designated S3 buckets for data processing. Benchling dashboards provide overviews of NGS sample preparation, data generation and quality control. In summary, Benchling interconnects the original sample, the labels, the barcodes, the cDNA/DNA, the library, and all the QC results. We process NGS data using pipelines implemented in FONDA on a dockerized Amazon Web Services cloud platform. Analyses can be configured automatically from information exported by Benchling or launched manually. After data processing is completed, output files such as gene expression counts or variant calls are deposited into project-specific folders, ready for secondary analysis. In the current FONDA version (as of Nov 2020), we have developed pipelines for single cell multi-omics (CITE-seq and single-cell immune profiling) and bulk RNA-seq. The modular design of FONDA facilitates the development, the updating, and the extension of pipelines to new sequencing technologies. In summary, Benchling and FONDA enable high quality sample and NGS data flows from the lab for target identification, understanding mechanism of action, patient stratification and biomarker discovery. Availability and implementation: FONDA is implemented in Java and released under the Apache License 2.0. FONDA can be downloaded from GitHub at https://github.com/epam/fonda. Citation Format: Chandra Sekhar Pedamallu, Joon Sang Lee, Shu Yan, Adalis Maisonet, Aleksandr Sidoruk, Tengui Chen, Yulia Kamyshova, Mariia Zueva, Mark Magid, Quan Wan, Jeffrey Thompson, Valerie Zebrouck, Immanuel Gadaczek, Mikhail Alperovich, Brian McNatt, Alexei Protopopov, Donald Jackson, Jack Pollard. A comprehensive sample tracking and data processing workflow for next generation sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2280.

Next Generation Sequencing Data Analysis and its Applications in Agriculture

Next Generation sequencing (NGS) technologies are revolutionizing the acquisition of genomic data at relatively low cost. NGS technologies are rapidly changing approaches to complex genomic studies and generating a vast amount of data. New and more powerful bioinformatics proposals and tools are needed to handle such large biological collections of such huge amounts of data provided by NGS data analysis. In addition, specialized software tools and advanced computational resources are required for data integration. In this article, we are describing here the main computational approach for next generation sequencing data analysis and its role in agriculture for crop improvement.

A 3D physio-mimetic interpenetrating network-based platform to decode the pro and anti-tumorigenic properties of cancer-associated fibroblasts

Three-dimensional (3D) biomaterials with physiologically relevant and experimentally tractable biomechanical features are important platforms to advance our understanding of the influence of tissue mechanics in disease progression. Herein, an interpenetrating network (IPN) of collagen and alginate 3D culture system with tunable extracellular microstructure and mechanics is exploited as a tumor stroma proxy to study phenotypic plasticity of colorectal cancer-associated fibroblasts (CAF). In combination with Next Generation Sequencing (NGS) data analysis, we demonstrated that tuning the storage modulus of the IPN hydrogel between 49 and 419 Pa can trigger a reversible switch between an inflammatory (i-state, α-SMAlowIL-6high) and myofibroblastic (m-state, α-SMAhighIL-6low) state in CAF that is dependent on the polymer network confinement effect and ROS-HIF1-α mechanotransduction signaling axis. Secretome from m-state CAF upregulated several epithelial-mesenchymal-transition (EMT) transcripts and induced robust scattering in DLD-1, HCT116, and SW480 human colorectal adenocarcinoma, while the EMT-inducing capacity is muted in i-state CAF, suggestive of an anti-tumorigenic role. Our findings were further validated through Gene Expression Profiling Interactive Analysis (GEPIA), which showed that cytokines secreted at higher levels by i-state CAF are correlated (p < 0.05) with good overall colorectal cancer patient survival. Therefore, 3D network density and spatial cellular confinement are critical biophysical determinants that can profoundly influence CAF states, paracrine signaling, and EMT-inducing potential. Statement of significanceThe communication between cancer cells and cancer-associated fibroblasts (CAF) contributes to tumor metastasis. CAF represent a diverse population of cellular subsets that can either promote or restrain tumor progression. However, the origin and cause of CAF heterogeneity remain elusive, limiting CAF-directed therapies for clinical use. We studied the dynamic phenotypes of CAF using a 3D physio-mimetic culture platform consisting of an interpenetrating collagen-alginate network. Combined with transcriptomic stratification and correlative analysis using cancer patient dataset, we showed phenotypic interconversion between inflammatory and myofibroblastic states, with anti- and pro-tumorigenic functions, in human colorectal CAF. This multidisciplinary study reveals the functional diversity of colorectal CAF caused by biophysical cues. The finding will influence the development of new CAF biomarkers and cancer therapies.

Quantitative profiling of protease specificity.

Proteases are an important class of enzymes, whose activity is central to many physiologic and pathologic processes. Detailed knowledge of protease specificity is key to understanding their function. Although many methods have been developed to profile specificities of proteases, few have the diversity and quantitative grasp necessary to fully define specificity of a protease, both in terms of substrate numbers and their catalytic efficiencies. We have developed a concept of “selectome”; the set of substrate amino acid sequences that uniquely represent the specificity of a protease. We applied it to two closely related members of the Matrixin family–MMP-2 and MMP-9 by using substrate phage display coupled with Next Generation Sequencing and information theory-based data analysis. We have also derived a quantitative measure of substrate specificity, which accounts for both the number of substrates and their relative catalytic efficiencies. Using these advances greatly facilitates elucidation of substrate selectivity between closely related members of a protease family. The study also provides insight into the degree to which the catalytic cleft defines substrate recognition, thus providing basis for overcoming two of the major challenges in the field of proteolysis: 1) development of highly selective activity probes for studying proteases with overlapping specificities, and 2) distinguishing targeted proteolysis from bystander proteolytic events.

Deep Learning Applied on Next Generation Sequencing Data Analysis.

Deep learning is defined as the group of computational techniques allowing for the discovery of latent information within large amounts of data. Recently, many fields have seen the immense potential of deep learning to solve various tasks in ways which outperformed many other traditional methods. Genomic research could be the next frontier to take advantage of deep learning, as it has the perfect combination of vast amounts of data and diverse tasks. Here we present the platform we generatedto combine deep learning and genomic sequencing data. We tested the platform on publicly available sequencing data from the gut microbiome of cancer patients. We showed that our platform is capable of classifying patients with higher accuracy thanother methods, with some caveats. Overall, we believe genomic research is the next frontline for deep learning as there are exciting avenues waiting to be explored. We think that our platform, presented here, could serve as the basis for such future research.

Machine Learning in Early Genetic Detection of Multiple Sclerosis Disease: A Survey

Multiple sclerosis disease is a main cause of non-traumatic disabilities and one of the most common neurological disorders in young adults over many countries. In this work, we introduce a survey study of the utilization of machine learning methods in Multiple Sclerosis early genetic disease detection methods incorporating Microarray data analysis and Single Nucleotide Polymorphism data analysis and explains in details the machine learning methods used in literature. In addition, this study demonstrates the future trends of Next Generation Sequencing data analysis in disease detection and sample datasets of each genetic detection method was included .in addition, the challenges facing genetic disease detection were elaborated.

Abstract 2995: Dissecting renal cell carcinoma vulnerabilities: Using CRISPR/Cas9 to study resistance to glutaminase inhibition

Abstract The aim of this study was to identify genes, which when knock out confer resistance to a novel glutaminase inhibitor in Renal Cell Carcinoma (RCC). RCC represents around 3% of all malignancies worldwide, with its most common type, clear cell RCC (ccRCC), making up around 70-80% of all RCC. The most frequent genomic alteration in ccRCC involves loss of VHL gene in &amp;gt;90% of ccRCC cases. Large-scale cancer genomics sequencing studies have identified several driver genes beyond VHL, particularly PBRM1 (40%), SETD2 (15%), and BAP1 (10%). RCC is characterised by significant tumour heterogeneity and inherent (in 25-30% cases) or acquired resistance to available chemotherapeutics. Glutamine addiction is a potential new therapeutic target for RCC as it has been recently shown not only to rely on glutamine for energy generation and maintenance of redox homeostasis, but also de novo pyrimidine synthesis. CB-839 is a small, orally administered reversible inhibitor of human kidney-type glutaminase (GLS). CB-839 is currently in early phase clinical trials and shows promising results. Given the significant incidence of resistance to previously approved therapies, we have applied a genome-scale CRISPR/Cas9 approach in a cell culture model of ccRCC (786-0 cell line) to identify candidate genes, which when knocked down confer resistance to CB-839. Next generation sequencing data analysis of drug-selected sgRNA library representation from two timepoints was performed using the MAGeCKFlute bioinformatics workflow. We conducted the screen in two biological replicates and verified successful genome-scale sgRNA library coverage at baseline in both cases. As a result, we identified candidate genes and pathways that are implicated in tumour metabolism, autophagy and metastasis. Additionally, this analysis identified KD genes, which in combination with CB-839 are synthetic lethal. We have prioritized the top 12 candidate genes, which we are validating in four cell lines (786-0, A498, A704 and Caki-1). After establishing CRISPR/Cas9 single gene knockdown (KD) cell lines, KD is confirmed using RT-qPCR and Western Blot, followed by functional analysis. KD cells were treated with increasing concentrations of the drug in parallel with a cell line containing a scrambled sgRNA to detect growth changes between the two. After in vitro validation, we performed ex vivo validation of the candidate hits. Our data show that the genome-scale CRISPR/Cas9 approach is effective in identifying candidates modulating drug resistance to CB-839. Citation Format: Aleksandra M. Raczka, Paul A. Reynolds. Dissecting renal cell carcinoma vulnerabilities: Using CRISPR/Cas9 to study resistance to glutaminase inhibition [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2995.

Accelerating next generation sequencing data analysis: an evaluation of optimized best practices for Genome Analysis Toolkit algorithms.

Advancements in next generation sequencing (NGS) technologies have significantly increased the translational use of genomics data in the medical field as well as the demand for computational infrastructure capable processing that data. To enhance the current understanding of software and hardware used to compute large scale human genomic datasets (NGS), the performance and accuracy of optimized versions of GATK algorithms, including Parabricks and Sentieon, were compared to the results of the original application (GATK V4.1.0, Intel x86 CPUs). Parabricks was able to process a 50× whole-genome sequencing library in under 3 h and Sentieon finished in under 8 h, whereas GATK v4.1.0 needed nearly 24 h. These results were achieved while maintaining greater than 99% accuracy and precision compared to stock GATK. Sentieon’s somatic pipeline achieved similar results greater than 99%. Additionally, the IBM POWER9 CPU performed well on bioinformatic workloads when tested with 10 different tools for alignment/mapping.

The Roles of RUNX Family Proteins in Development of Immune Cells.

The Runt-related transcription factors (RUNX) transcription factors have been known for their critical roles in numerous developmental processes and diseases such as autoimmune disorders and cancer. Especially, RUNX proteins are best known for their roles in hematopoiesis, particularly during the development of T cells. As scientists discover more types of new immune cells, the functional diversity of RUNX proteins also has been increased over time. Furthermore, recent research has revealed complicated transcriptional networks involving RUNX proteins by the current technical advances. Databases established by next generation sequencing data analysis has identified ever increasing numbers of potential targets for RUNX proteins and other transcription factors. Here, we summarize diverse functions of RUNX proteins mainly on lymphoid lineage cells by incorporating recent discoveries.

Abstract 1259: Loss of TFF1 promotes growth and chemotherapeutic resistance in gastric cancer

Abstract Background: Downregulation or loss of TFF1 expression occurs in more than half of human gastric adenocarcinomas through gene deletions, mutation, and a loss of heterozygosity or hypermethylation. Our previous studies have shown that Tff1 knockout (KO) in mice induces gastric lesions from low- grade dysplasia (LGD), high- grade dysplasia (HGD) to adenocarcinoma. BRD2, a family member of BET proteins, promotes aberrant gene expression in a variety of malignant tumors. In this study, with the analysis of Tff1 KO mice and human gastric cancer tissue samples, we discovered that loss of Tff1 promotes gastric cancer proliferation and drug resistance through upregulating BRD2 expression level. Methods and Results: Integration Next Generation Sequencing data analysis in both Tff1 KO mice and human gastric cancer tissue samples demonstrated that miR-143-3p was significantly down-regulated in both mice and human gastric cancer samples (P&amp;lt;0.05). Our findings were further validated by RT-PCR in Tff1 KO mouse gastric legions (LGD and adenocarcinoma) and 3 different cohorts of human gastric cancer tissue samples from USA, Chile, and China. Using different gastric cancer cell models we further confirmed the decrease of miR-143-3p regulated by loss of TFF1. Next, we wanted to find out the downstream effector of miR-143-3p down-regulation. Western blot data showed that BRD2 protein level was regulated by miR-143-3p in 4 gastric cancer cell lines. BRD2 3'UTR luciferase reporter assay further confirmed that miR-143-3p decreased BRD2 protein expression through directly binding to its 3'UTR sites. These data suggested that, for the first time, BRD2 is a direct downstream target of miR-143-3p. At the meantime, the reconstitution of TFF1 in gastric cancer cells up-regulated miR-143-3p expression level, which in turn, decreased the protein expression levels of BRD2, MYC and BCL-2. Using western blot, we showed the synergistic effect of BRD2 inhibitor and CDDP chemotherapy in human gastric cancer cells. Data from 320 human gastric cancer patients demonstrated that high BRD2 expression level in gastric cancer tissues significantly decreased the overall patient survival rate (P&amp;lt;0.0001). Conclusion: This study unveils, for the first time, loss of TFF1 promotes BRD2 activation in gastric cancer through decreasing miR-143-3p. This axis presents novel therapeutic opportunities by using approaches that reconstitute miR-143-3p or utilizing the recently developed clinical trials in human gastric cancer. Citation Format: Zheng Chen, Zheng Li, Mohammed Soutto, Weizhi Wang, Shoumin Zhu, Alejandro Corvalan, Zekuan Xu, Wael El-Rifai. Loss of TFF1 promotes growth and chemotherapeutic resistance in gastric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1259.

FISH mapping of 45S rRNA and IHHB genes on autosomes and B chromosome of cichlid fish Astatotilapia latifasciata

B chromosomes (B) are supernumerary, additional and non-homologous to standard (A) chromosomes. Significant biological questions concerning the origin and structural organization of Bs are under discussion. Previous reports found that B chromosomes are enriched with repetitive elements and can carry functional genes. The present study is aimed to extend our knowledge of the structure and composition of B chromosomes. We have combined cytogenetics and genomics approaches to undertsand the B chromosome problem. Cytogenetics analysis, applied to cichlid fish Astatotilapia latifasciata, revealed the presence of 1 or 2 B chromosomes. Next generation sequencing data analysis, based on differential coverage approach, among B- (individual without B) and B+ (individual with B) samples, detected a higher number of copies for the Indian Hedgehog b (Ihhb) gene and 45S RNA transcriptional gene-cluster. Fluorescence in situ hybridization (FISH) confirmed that these genes are located on the B chromosome. In addition, the excessively abundant and strong signals of IHHB on B shows that this gene has accumulated and amplified its copies due to frequent duplication events. The findings also demonstrate that B chromosome have incorporated the entire 45S RNA cluster (18S ribosomal RNA, internal transcribed spacer 1, 5.8S ribosomal RNA, internal transcribed spacer 2, and 28S ribosomal RNA) from the A complement set. Our study contributes to explain that these genic sequences are vital components in constitution of B chromosomes.

Accelerating next generation sequencing data analysis with system level optimizations

Next generation sequencing (NGS) data analysis is highly compute intensive. In-memory computing, vectorization, bulk data transfer, CPU frequency scaling are some of the hardware features in the modern computing architectures. To get the best execution time and utilize these hardware features, it is necessary to tune the system level parameters before running the application. We studied the GATK-HaplotypeCaller which is part of common NGS workflows, that consume more than 43% of the total execution time. Multiple GATK 3.x versions were benchmarked and the execution time of HaplotypeCaller was optimized by various system level parameters which included: (i) tuning the parallel garbage collection and kernel shared memory to simulate in-memory computing, (ii) architecture-specific tuning in the PairHMM library for vectorization, (iii) including Java 1.8 features through GATK source code compilation and building a runtime environment for parallel sorting and bulk data transfer (iv) the default ’on-demand’ mode of CPU frequency is over-clocked by using ’performance-mode’ to accelerate the Java multi-threads. As a result, the HaplotypeCaller execution time was reduced by 82.66% in GATK 3.3 and 42.61% in GATK 3.7. Overall, the execution time of NGS pipeline was reduced to 70.60% and 34.14% for GATK 3.3 and GATK 3.7 respectively.

Next Generation Sequencing Data Analysis Evaluation in Patients with Parkinsonism from a Genetically Isolated Population

Parkinson's disease (PD) can be caused by genetic changes in a lot of genes. The effect of these changes is determined by the nature of the mutation and ranges from weak associations to pathogenic mutation which leads to loss of protein function. Our study is based on epidemiological data which show significantly increased prevalence of PD (2.9 %) in an isolated population of South-Eastern Moravia in the Czech Republic. We compared two different Next Generation Sequencing (NGS) data analysis approaches in DNA from 28 PD patients in the genes responsible for Parkinsonism (ADH1C, ATP13A2, EIF4G1, FBXO7, GBA + GBAP1, GIGYF2, HTRA2, LRRK2, MAPT, PARK2, PARK7, PINK1, PLA2G6, SNCA, UCHL1 and VPS35) using: 1) already described missense rare variants or pathogenic mutations 2) twelve control DNA samples from the same isolated population. Ion Torrent NGS data processing and trimming from Fastaq through “bam” to “vcf” files was done parallely by Torrent Suite/Ion Reporter and NextGENe software. After filtering out, three missense mutations were found in LRRK2 gene: rs33995883 in 6/0 patients/control (p/c); rs33958906 in 1/1p/c; rs781737269 in 3/0p/c; one missense mutation in MAPT gene rs63750072 in 6/1p/c; and one mutation in HTRA2 gene rs72470545 in 3/1p/c. Both the results from NextGENe with Ion Torrent adaptation and from Ion Reporter significantly correlated in variant calling. Our study may contribute to further explain the genetic background of Parkinsonism.

Genetic testing of 248 Chinese aortopathy patients using a panel assay.

Inherited aortopathy, which is characterized by a high risk of fatal aortic aneurysms/dissections, can occur secondarily to several syndromes. To identify genetic mutations and help make a precise diagnosis, we designed a gene panel containing 15 genes responsible for inherited aortopathy and tested 248 probands with aortic disease or Marfan syndrome. The results showed that 92 individuals (37.1%) tested positive for a (likely) pathogenic mutation, most of which were FBN1 mutations. We found that patients with a FBN1 truncating or splicing mutation were more prone to developing severe aortic disease or valvular disease. To date, this is the largest reported cohort of Chinese patients with aortic disease who have undergone genetic testing. Therefore, it can serve as a considerable dataset of next generation sequencing data analysis of Chinese population with inherited aortopathy. Additionally, according to the accumulated data, we optimized the analysis pipeline by adding quality control steps and lowering the false positive rate.

Detection of Natural Resistance-Associated Substitutions by Ion Semiconductor Technology in HCV1b Positive, Direct-Acting Antiviral Agents-Naïve Patients.

Naturally occurring resistance-associated substitutions (RASs) can negatively impact the response to direct-acting antivirals (DAAs) agents-based therapies for hepatitis C virus (HCV) infection. Herein, we set out to characterize the RASs in the HCV1b genome from serum samples of DAA-naïve patients in the context of the SINERGIE (South Italian Network for Rational Guidelines and International Epidemiology, 2014) project. We deep-sequenced the NS3/4A protease region of the viral population using the Ion Torrent Personal Genome Machine, and patient-specific majority rule consensus sequence summaries were constructed with a combination of freely available next generation sequencing data analysis software. We detected NS3/4A protease major and minor variants associated with resistance to boceprevir (V36L), telaprevir (V36L, I132V), simeprevir (V36L), and grazoprevir (V36L, V170I). Furthermore, we sequenced part of HCV NS5B polymerase using Sanger-sequencing and detected a natural RAS for dasabuvir (C316N). This mutation could be important for treatment strategies in cases of previous therapy failure.

A novel procedure on next generation sequencing data analysis using text mining algorithm

BackgroundNext-generation sequencing (NGS) technologies have provided researchers with vast possibilities in various biological and biomedical research areas. Efficient data mining strategies are in high demand for large scale comparative and evolutional studies to be performed on the large amounts of data derived from NGS projects. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining.MethodsWe report a novel procedure to analyse NGS data using topic modeling. It consists of four major procedures: NGS data retrieval, preprocessing, topic modeling, and data mining using Latent Dirichlet Allocation (LDA) topic outputs. The NGS data set of the Salmonella enterica strains were used as a case study to show the workflow of this procedure. The perplexity measurement of the topic numbers and the convergence efficiencies of Gibbs sampling were calculated and discussed for achieving the best result from the proposed procedure.ResultsThe output topics by LDA algorithms could be treated as features of Salmonella strains to accurately describe the genetic diversity of fliC gene in various serotypes. The results of a two-way hierarchical clustering and data matrix analysis on LDA-derived matrices successfully classified Salmonella serotypes based on the NGS data. The implementation of topic modeling in NGS data analysis procedure provides a new way to elucidate genetic information from NGS data, and identify the gene-phenotype relationships and biomarkers, especially in the era of biological and medical big data.ConclusionThe implementation of topic modeling in NGS data analysis provides a new way to elucidate genetic information from NGS data, and identify the gene-phenotype relationships and biomarkers, especially in the era of biological and medical big data.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1075-9) contains supplementary material, which is available to authorized users.

Detection Copy Number Variants from NGS with Sparse and Smooth Constraints.

It is known that copy number variations (CNVs) are associated with complex diseases and particular tumor types, thus reliable identification of CNVs is of great potential value. Recent advances in next generation sequencing (NGS) data analysis have helped manifest the richness of CNV information. However, the performances of these methods are not consistent. Reliably finding CNVs in NGS data in an efficient way remains a challenging topic, worthy of further investigation. Accordingly, we tackle the problem by formulating CNVs identification into a quadratic optimization problem involving two constraints. By imposing the constraints of sparsity and smoothness, the reconstructed read depth signal from NGS is anticipated to fit the CNVs patterns more accurately. An efficient numerical solution tailored from alternating direction minimization (ADM) framework is elaborated. We demonstrate the advantages of the proposed method, namely ADM-CNV, by comparing it with six popular CNV detection methods using synthetic, simulated, and empirical sequencing data. It is shown that the proposed approach can successfully reconstruct CNV patterns from raw data, and achieve superior or comparable performance in detection of the CNVs compared to the existing counterparts.