Next-generation Sequencing Data Analysis Research Articles

Molecular profiling as a predictor of treatment response and survival in unresectable pancreatic ductal adenocarcinoma.

e16279 Background: To date, few targetable mutations have been found to enhance PDAC treatment. Next-generation sequencing (NGS) of somatic mutation profiles may help tailor cytotoxic chemotherapy selection and guide translational research. Methods: We analyzed NGS data from a retrospective cohort of 142 patients with unresectable biopsy-proven PDAC from August 2009 to December 2022. Individual mutations were grouped into 14 distinct signaling pathways: cell cycle, DNA damage repair, KIT, ErbB2, TP53, MAPK-RAS, PIK3, TGF-beta, NOTCH, ALK, FLT3, FGFR, and WNT. Treatment selection, duration of response, and survival were recorded. We stratified patients based on their first-line therapy, either FOLFIRINOX (FFX) or gemcitabine/nab-paclitaxel (GA). We present median time-to-first progression (PFS1) and overall survival (OS) based on the presence or absence of mutations in these pathways. The time-to-event endpoint was estimated by the Kaplan-Meier method. Results: Of 142 samples, 131 were analyzed for PFS1; 57 received FFX, and 60 received GA. In the FFX group, no pathway predicted PFS1 with statistical significance. In the GA group, KIT pathway mutations (n = 10) showed improved PFS1 vs. non-KIT mutated tumors (10.3 mos vs. 6.2 mos, p = 0.02). Similar improvement in PFS1 was seen for mutations in the PIK3 pathway (n = 25, 6.6 vs. 5.7, p = 0.02) and FLT3 pathway (n = 4, 13.1 vs. 6.2, p = 0.02). 125 samples were analyzed for OS. KIT pathway mutations (n = 21) showed a better OS vs. non-KIT mutated samples (21.3 mos vs. 12.2 mos, p = 0.04), as did NOTCH mutated samples (n = 44, 15 vs. 12.3, p < 0.01). FLT3 pathway mutations showed a trend toward improved OS (23.6 vs. 13, p = 0.3). PIK3 mutated tumors had no difference in OS. 8 samples were KRAS WT; removing these from the analysis did not change the results. Conclusions: In this sample, KIT, FLT3, and PIK3 pathway mutated tumors had improved PFS1 with GA (but not FFX) treatment. Tumors with KIT and NOTCH pathway mutations had an improved OS. This and similar analyses could help identify potential drivers of treatment response for unresectable pancreatic tumors. [Table: see text]

Racial differences in prognostic role of white blood cell ratios in gastrointestinal stromal tumors.

e23507 Background: Gastrointestinal stromal tumors (GIST) are rare mesenchymal tumors with an estimated 3,300 to 6,000 new cases per year in the United States. Per SEER database, significant racial disparity exists with an incidence rate twice as high for African Americans as compared to Whites. Currently, parameters used in stratification of disease risk can only be determined by postoperative histopathological evaluation. Cancer-related inflammation is linked to different stages of tumor development so markers found in peripheral blood have been studied as surrogates. We present a study comparing the predictive prognostic ability of preoperative serum inflammatory markers in primary untreated GIST and highlight the differences between Black and White patients. Methods: This is a retrospective analysis of 149 patients who underwent operative resection of primary untreated GIST. Preoperative blood samples were used for the analysis. Patients were excluded if they showed clinical signs of infection during time of pre-operative blood sampling or had non-GIST synchronous malignancy. Clinicopathological data was obtained from patient chart review and logistic regression models were used to evaluate data. There is ongoing next-generation sequencing data analysis. Results: 149 patients were included: 80 (53.7%) Black, 43 (28.9%) White, 26 (17.5%) were other. The mean values for neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), and monocyte to lymphocyte ratio (MLR) were 3.62 (SD 3.07), 191.55 (SD 119.15), and 0.47 (SD 0.49) respectively. On univariate analysis NLR and PLR were significant prognostic indicators of high and intermediate risk GISTs per the National Institute of Health (NIH) categories [NLR p = 0.008, PLR p = 0.007]. Upon subgroup analysis, NLR and PLR showed the highest statistical significance for predictors of high and intermediate risk GISTs among the Black patient population [NLR odds ratio (OR) 2.685; 95% confidence interval (CI) 0.976-7.381; p = 0.05 and PLR OR 1.792; 95% CI 1.054-3.049; p = 0.031]. The same was not shown to be true for the White patient population. 11 out 149 patients progressed since diagnosis of which 8 were Black and 3 White. 7 were in the high risk category, 3 in low risk and 1 in intermediate risk with an overall mean NLR and PLR 5.25 and 244 respectively. The average time to progression was noted to 28 months. Conclusions: The NLR and PLR are easily obtainable and cost-effective indexes which have the prognostic ability to serve as surrogates for high and intermediate risk GISTs. They appear to be more predictive in the Black patient population than in White. Further study is warranted for incorporation into risk stratification systems.

Detection of Potential Viral Sequence from Next Generation Sequencing Data Using Convolutional Neural Network

Next Generation Sequencing (NGS) is a modern sequencing technology that can determine the sequences of RNA and DNA faster and at lower cost. The availability of NGS data has sparked numerous efforts in bioinformatics, especially in the study of genetic variation and viral sequence detection. Viral sequence detection has been one of the important processes in studying virus-induced diseases. Common methods in detecting viral sequences involve alignment of the sequence with existing databases, which remains limited as these databases might be incomplete and difficult to detect highly divergent viruses. Thus, machine learning and deep learning have been used in this regard, to unveil the patterns that distinguish viral sequences through learning from the NGS data. This study focuses on viral sequence detection using convolutional neural network (CNN). This study intended to investigate how CNN model can be used for analysis of NGS data and develop a CNN model for detecting potential viral sequences from NGS data. The CNN architecture used for this study is based on an existing design that divided into two branches namely pattern and frequency branch that cater for extracting different aspects of information from the data and lastly combined into a full model. This study further implemented slightly modified architecture that includes additional convolution layer and pooling layer. Then, parameter tuning is implemented to identify near optimal parameters for the CNN to elucidate the performance impact. The evaluation of the optimized CNN model is done using a dataset with 18,445 DNA sequences. The results show that the CNN model in this study achieved a better performance compared with existing in terms of area under receiver operating characteristics curve (AUROC) for full model (+0.1434).

Identification of Macrocyclic Peptide Families from Combinatorial Libraries Containing Noncanonical Amino Acids Using Cheminformatics and Bioinformatics Inspired Clustering.

In the past decade, macrocyclic peptides gained increasing interest as a new therapeutic modality to tackle intracellular and extracellular therapeutic targets that had been previously classified as "undruggable". Several technological advances have made discovering macrocyclic peptides against these targets possible: 1) the inclusion of noncanonical amino acids (NCAAs) into mRNA display, 2) increased availability of next generation sequencing (NGS), and 3) improvements in rapid peptide synthesis platforms. This type of directed-evolution based screening can produce large numbers of potential hit sequences given that DNA sequencing is the functional output of this platform. The current standard for selecting hit peptides from these selections for downstream follow-up relies on the frequency counting and sorting of unique peptide sequences which can result in the generation of false negatives due to technical reasons including low translation efficiency or other experimental factors. To overcome our inability to detect weakly enriched peptide sequences among our large data sets, we wanted to develop a clustering method that would enable the identification of peptide families. Unfortunately, utilizing traditional clustering algorithms, such as ClustalW, is not possible for this technology due to the incorporation of NCAAs in these libraries. Therefore, we developed a new atomistic clustering method with a Pairwise Aligned Peptide (PAP) chemical similarity metric to perform sequence alignments and identify macrocyclic peptide families. With this method, low enriched peptides, including isolated sequences (singletons), can now be clustered into families providing a comprehensive analysis of NGS data resulting from macrocycle discovery selections. Additionally, upon identification of a hit peptide with the desired activity, this clustering algorithm can be used to identify derivatives from the initial data set for structure-activity relationship (SAR) analysis without requiring additional selection experiments.

MSdb: An integrated expression atlas of human musculoskeletal system

The global prevalence and burden of musculoskeletal (MSK) disorders are immense. Advancements in next-generation sequencing (NGS) have generated vast amounts of data, accelerating the research of pathological mechanisms and the development of therapeutic approaches for MSK disorders. However, scattered datasets across various repositories complicate uniform analysis and comparison. Here, we introduce MSdb, a database for visualization and integrated analysis of next-generation sequencing data from human musculoskeletal system, along with manually curated patient phenotype data. MSdb provides various types of analysis, including sample-level browsing of metadata information, gene/miRNA expression, and single-cell RNA-seq dataset. In addition, MSdb also allows integrated analysis for cross-samples and cross-omics analysis, including customized differentially expressed gene/microRNA analysis, microRNA-gene network, scRNA-seq cross-sample/disease integration, and gene regulatory network analysis. Overall, systematic categorizing, standardized processing, and freely accessible knowledge features MSdb a valuable resource for MSK research community.

Unraveling microbial community by next-generation sequencing in living membrane bioreactors for wastewater treatment

This study delves into the microbial community complexity and its role in self-forming dynamic membrane (SFDM) systems, designed to remove nutrients and pollutants from wastewater, by means of the analysis of Next-Generation Sequencing (NGS) data. In these systems, microorganisms are naturally incorporated into the SFDM layer, which acts as a biological and physical filter. The microorganisms present in an innovative and highly efficient aerobic, electrochemically enhanced, encapsulated SFDM bioreactor were studied to elucidate the nature of the dominant microbial communities present in sludge and in encapsulated SFDM, patented as living membrane® (LM) of the experimental setup. The results were compared to those obtained from the microbial communities found in similar experimental reactors without an applied electric field. The data gathered from the NGS microbiome profiling showed that the microbial consortia found in the experimental systems are comprised of archaeal, bacterial, and fungal communities. However, the distribution of the microbial communities found in e-LMBR and LMBR had significant differences. The results showed that the presence of an intermittently applied electric field in e-LMBR promotes the growth of some types of microorganisms (mainly electroactive microorganisms) responsible for the highly efficient treatment of the wastewater and for the mitigation of the membrane fouling found for those bioreactors.

Genetic investigation of 124 SNPs in a Myanmar population using the Precision ID Identity Panel and the Illumina MiSeq

Single nucleotide polymorphisms (SNPs) have become popular in forensic genetics as an alternative to short tandem repeats (STRs). The Precision ID Identity Panel (Thermo Fisher Scientific), consisting of 90 autosomal SNPs and 34 Y-chromosomal SNPs, enabled human identification studies on global populations through next-generation sequencing (NGS). However, most previous studies on the panel have used the Ion Torrent platform, and there are few reports on the Southeast Asian population. Here, a total of 96 unrelated males from Myanmar (Yangon) were analyzed with the Precision ID Identity Panel on a MiSeq (Illumina) using an in-house TruSeq compatible universal adapter and a custom variant caller, Visual SNP. The sequencing performance evaluated by locus balance and heterozygote balance was comparable to that of the Ion Torrent platform. For 90 autosomal SNPs, the combined match probability (CMP) was 6.994 × 10−34, lower than that of 22 PowerPlex Fusion autosomal STRs (3.130 × 10−26). For 34 Y-SNPs, 14 Y-haplogroups (mostly O2 and O1b) were observed. We found 51 cryptic variations (42 haplotypes) around target SNPs, of which haplotypes corresponding to 33 autosomal SNPs decreased CMP. Interpopulation analysis revealed that the Myanmar population is genetically closer to the East and Southeast Asian populations. In conclusion, the Precision ID Identity Panel can be successfully analyzed on the Illumina MiSeq and provides high discrimination power for human identification in the Myanmar population. This study broadened the accessibility of the NGS-based SNP panel by expanding the available NGS platforms and adopting a robust NGS data analysis tool.

Abstract 2081: Nano2NGS: A framework for converting nanopore sequencing data to NGS-liked sequencing data for data analysis

Abstract Background: Nanopore sequencing, also known as single molecule real-time sequencing, enables deciphering single DNA/RNA molecules without the polymerase chain reaction (PCR). Although nanopore sequencing has made significant progress in some areas, its application has been limited compared with next-generation sequencing (NGS) due to specific design principle and data characteristics, especially in hotspot mutation detection, metagenomic analysis, short tandem repeat (STR) detection and human leukocyte antigen (HLA) typing. Methods: The Nano2NGS framework included 4 modules, namely Nano2NGS-Muta, -Meta, -STR and -HLA. For hotspot mutation detection, we tested Nano2NGS-Muta + Freebayes using simulated data including 12 hotspot mutations (SNVs and InDels), and standard sample including 7 hotspot mutations with ~5% mutation frequency. For metagenomic analysis, we tested Nano2NGS-Meta + Kraken2 using simulated data including 20 bacterial species from the NCBI database, and standard sample including 7 bacteria with ~14% theoretical microbial composition. For STR detection, we tested Nano2NGS-STR using simulated data including 4 STR markers (DYS392, DYS438, DYS448, and DYS635), and standard sample including 44 STR markers from the intersection of two commercial products. For HLA typing, we tested Nano2NGS-HLA + Athlates using 1 simulated data and 1 standard sample. The GM12878 gDNA sample was used as negative control. All samples have 3 experimental repetitive. The nanopore sequencing was performed on the QNome-9604/3841 instrument according to the manufacturer’s instructions. Results: The performance of all 4 modules in simulated datasets was perfect. In standard sample datasets, for hotspot mutation detection, we found the repeated experiments detection sensitivity was 95.24%, 100.00% and 85.71%, respectively. The limit of detection (LoD) of Nano2NGS-Muta + Freebayes may be 2% or even 5% according to the negative dataset. For metagenomic analysis, we found the overall performance of Nano2NGS-Meta + Kraken2 was better, and R-square value compared with expected relative abundance was 0.9357. For STR detection, we found Nano2NGS-STR achieved the best performance compared with Tandem-Genotypes and TriCoLOR, with 71.97% (9948) and 53.03% (2800M) concordance, respectively. For HLA typing, we found the result of Nano2NGS-HLA + Athlates was HLA-A*02/HLA-A*11, HLA-B*35/HLA-B*39 and HLA-C*07/HLA-C*12, which were consistent with the expected genotypes of allele group (field 1). Conclusions: Nano2NGS framework converts nanopore sequencing data into NGS-liked short-read data and can be compatible with the NGS data analysis software. Importantly, Nano2NGS breaks the barriers of data analysis methods between short-read sequencing and long-read sequencing, and accelerates the application of nanopore sequencing technology in scientific research and clinical practice. Citation Format: Jidong Lang. Nano2NGS: A framework for converting nanopore sequencing data to NGS-liked sequencing data for data analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2081.

Methods for analyzing next-generation sequencing data 20. Properties and statistical models of RNA-seq count data.

Methods for analyzing next-generation sequencing data 20. Properties and statistical models of RNA-seq count data.

Analysis of well-annotated next-generation sequencing data reveals increasing cases of SARS-CoV-2 reinfection with Omicron

SARS-CoV-2 has extensively mutated creating variants of concern (VOC) resulting in global infection surges. The Omicron VOC reinfects individuals exposed to earlier variants of SARS-CoV-2 at a higher frequency than previously seen for non-Omicron VOC. An analysis of the sub-lineages associated with an Omicron primary infection and Omicron reinfection reveals that the incidence of Omicron-Omicron reinfections is occurring over a shorter time interval than seen after a primary infection with a non-Omicron VOC. Our analysis suggests that a single infection from SARS-CoV-2 may not generate the protective immunity required to defend against reinfections from emerging Omicron lineages. This analysis was made possible by Next-generation sequencing (NGS) of a Danish cohort with clinical metadata on both infections occurring in the same individual. We suggest that the continuation of COVID-19 NGS and inclusion of clinical metadata is necessary to ensure effective surveillance of SARS-CoV-2 genomics, assist in treatment and vaccine development, and guide public health recommendations.

On potential limitations of differential expression analysis with non-linear machine learning models

Recently, there has been a growing interest in bioinformatics toward the adoption of increasingly complex machine learning models for the analysis of next-generation sequencing data with the goal of disease subtyping (i.e., patient stratification based on molecular features) or risk-based classification for specific endpoints, such as survival. With gene-expression data, a common approach consists in characterising the emerging groups by exploiting a differential expression analysis, which selects relevant gene sets coupled with pathway enrichment analysis, providing an insight into the underlying biological processes. However, when non-linear machine learning models are involved, differential expression analysis could be limiting since patient groupings identified by the model could be based on a set of genes that are hidden to differential expression due to its linear nature, affecting subsequent biological characterisation and validation. The aim of this study is to provide a proof-of-concept example demonstrating such a limitation. Moreover, we suggest that this issue could be overcome by the adoption of the innovative paradigm of eXplainable Artificial Intelligence, which consists in building an additional explainer to get a trustworthy interpretation of the model outputs and building a reliable set of genes characterising each group, preserving also non-linear relations, to be used for downstream analysis and validation.

Adaptation to the Progress in Cancer Genomic Medicine by a Japanese Community Hospital

Background: Remarkable progress in cancer genomic medicine (CGM) has been made with the advent of next-generation sequencing and advanced computational data analysis approaches. In Japan gene panel testing has been covered by the National Health Insurance System since June 2019. Although Nagoya Memorial Hospital has been designated as a regional medical support hospital, their medical staff are unfamiliar with CGM and generally experience difficulty in explaining the genetic testing to cancer patients.
 Methods: A multi-disciplinary CGM team was created in July 2019 to adapt to the clinical application of gene panel testing. Hospital functions were then maintained focusing on the following three aspects: a pathology system for handling genetic information, human resource development related to CGM, and a patient support system, including genetic counseling.
 Results: Third party ISO15189 (International Organization for Standardization) certification was acquired for the Department of Pathology to establish a quality-assured laboratory. Here, we report on 21 cancer patients who consulted and received information from the CGM outpatient department of our hospital. Among them 14 patients were introduced into a group of certified hospitals by the Japanese Ministry of Health, Labour, and Welfare and 10 patients underwent gene panel tests.
 Conclusions: As a regional medical support hospital dealing with many cancer patients, we will further improve hospital functions to match the progress in CGM.

Standards of NGS Data Sharing and Analysis in Ataxias: Recommendations by the NGS Working Group of the Ataxia Global Initiative

The Ataxia Global Initiative (AGI) is a worldwide multi-stakeholder research platform to systematically enhance trial-readiness in degenerative ataxias. The next-generation sequencing (NGS) working group of the AGI aims to improve methods, platforms, and international standards for ataxia NGS analysis and data sharing, ultimately allowing to increase the number of genetically ataxia patients amenable for natural history and treatment trials. Despite extensive implementation of NGS for ataxia patients in clinical and research settings, the diagnostic gap remains sizeable, as approximately 50% of patients with hereditary ataxia remain genetically undiagnosed. One current shortcoming is the fragmentation of patients and NGS datasets on different analysis platforms and databases around the world. The AGI NGS working group in collaboration with the AGI associated research platforms—CAGC, GENESIS, and RD-Connect GPAP—provides clinicians and scientists access to user-friendly and adaptable interfaces to analyze genome-scale patient data. These platforms also foster collaboration within the ataxia community. These efforts and tools have led to the diagnosis of > 500 ataxia patients and the discovery of > 30 novel ataxia genes. Here, the AGI NGS working group presents their consensus recommendations for NGS data sharing initiatives in the ataxia field, focusing on harmonized NGS variant analysis and standardized clinical and metadata collection, combined with collaborative data and analysis tool sharing across platforms.

What Mendel could learn from the inborn errors of immunity – harnessing the power of NGS genetic testing for diagnosis of immune disorders

The inborn errors of immunity (IEIs) include primary immunodeficiencies (PIDs) and primary immune regulatory disorders (PIRDs). Though there are somatic variations in genes that cause immunological disease, the IEIs, for the most part, describe germline gene defects. The IEIs now include 485 genes and the list continues to grow. These genes are classified into nine categories based on the component of the immune system dominantly affected (IUIS classification 2022). The explosion in the number of genes identified in the past two decades has to do with rapid advances in genomic technologies, which allow for high throughput analysis, such as next generation sequencing (NGS). NGS includes targeted gene panels (TGPs), exome (ES) and genome sequencing (GS), besides copy number variation (CNV) analysis. Since the etiological basis of most, if not all, IEIs is related to a single gene defect (monogenic) or in some cases, two genes (digenic), it stands to reason that diagnosis of these disorders requires genetic testing for confirmation of the diagnosis. However, a confirmed genetic answer is not just for diagnosis but also for selecting appropriate therapeutic and management strategies, which facilitates personalised approaches to treatment. This talk will provide an overview of the rationale for genetic testing for the diagnosis of IEIs, the most commonly used NGS methodologies and data analysis, which includes variant classification and interpretation of variants of uncertain significance (VUS). The talk will also provide information on various tools and databases used for genomic data analysis, and the concepts of incomplete penetrance, variable expressivity, somatic reversion, somatic mosaicism, hypopmoprhic variants, gain-of-function and loss-of-function.

Selection, Identification and Functional Performance of Ammonia-Degrading Microbial Communities from an Activated Sludge for Landfill Leachate Treatment.

The increasing amounts of municipal solid waste and their management in landfills caused an increase in the production of leachate, a liquid formed by the percolation of rainwater through the waste. Leachate creates serious problems to municipal wastewater treatment plants; indeed, its high levels of ammonia are toxic for bacterial cells and drastically reduce the biological removal of nitrogen by activated sludge. In the present work, we studied, using a metagenomic approach based on next-generation sequencing (NGS), the microbial composition of sludge in the municipal wastewater treatment plant of Porto Sant'Elpidio (Italy). Through activated sludge enrichment experiments based on the Repetitive Re-Inoculum Assay, we were able to select and identify a minimal bacterial community capable of degrading high concentrations of ammonium (NH4+-N ≅ 350 mg/L) present in a leachate-based medium. The analysis of NGS data suggests that seven families of bacteria (Alcaligenaceae, Nitrosomonadaceae, Caulobacteraceae, Xanthomonadaceae, Rhodanobacteraceae, Comamonadaceae and Chitinophagaceae) are mainly responsible for ammonia oxidation. Furthermore, we isolated from the enriched sludge three genera (Klebsiella sp., Castellaniella sp. and Acinetobacter sp.) capable of heterotrophic nitrification coupled with aerobic denitrification. These bacteria released a trace amount of both nitrite and nitrate possibly transforming ammonia into gaseous nitrogen. Our findings represent the starting point to produce an optimized microorganisms's mixture for the biological removal of ammonia contained in leachate.

Identification and Interaction Analysis of Molecular Markers in Pancreatic Ductal Adenocarcinoma by Bioinformatics and Next-Generation Sequencing Data Analysis.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most common cancers worldwide. Intense efforts have been made to elucidate the molecular pathogenesis, but the molecular mechanisms of PDAC are still not well understood. The purpose of this study is to further explore the molecular mechanism of PDAC through integrated bioinformatics analysis. To identify the candidate genes in the carcinogenesis and progression of PDAC, next-generation sequencing (NGS) data set GSE133684 was downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and Gene Ontology (GO) and pathway enrichment analyses were performed. The protein-protein interaction network (PPI) was constructed and the module analysis was performed using Integrated Interactions Database (IID) interactome database and Cytoscape. Subsequently, miRNA-DEG regulatory network and TF-DEG regulatory network were constructed using miRNet database, NetworkAnalyst database, and Cytoscape software. The expression levels of hub genes were validated based on Kaplan-Meier analysis, expression analysis, stage analysis, mutation analysis, protein expression analysis, immune infiltration analysis, and receiver operating characteristic (ROC) curve analysis. A total of 463 DEGs were identified, consisting of 232 upregulated genes and 233 downregulated genes. The enriched GO terms and pathways of the DEGs include vesicle organization, secretory vesicle, protein dimerization activity, lymphocyte activation, cell surface, transferase activity, transferring phosphorus-containing groups, hemostasis, and adaptive immune system. Four hub genes (namely, cathepsin B [CCNB1], four-and-a-half LIM domains 2 (FHL2), major histocompatibility complex, class II, DP alpha 1 (HLA-DPA1) and tubulin beta 1 class VI (TUBB1)) were obtained via taking interaction of different analysis results. On the whole, the findings of this investigation enhance our understanding of the potential molecular mechanisms of PDAC and provide potential targets for further investigation.

Mitochondrial DNA Sequencing and Heteroplasmy Quantification by Next Generation Sequencing.

Over the last 10years, next generation sequencing (NGS) became the gold standard for both diagnosis and discovery of new disease genes responsible for heterogeneous disorders, such as mitochondrial encephalomyopathies. The application of this technology to mtDNA mutations poses extra challenges compared to other genetic conditions because of the peculiarities of mitochondrial genetics and the requirement for proper NGS data management and analysis. Here, we describe a detailed, clinically relevant protocol to sequence the whole mtDNA and quantify heteroplasmy levels of mtDNA variants, starting from total DNA through the generation of a single PCR amplicon.

Next-Generation Sequencing Markup Language (NGSML): A Medium for the Representation and Exchange of NGS Data.

With the increasing demand for low-cost high-throughput sequencing of large genomes, next-generation sequencing (NGS) technology has developed rapidly. NGS can not only be used in basic scientific research but also in clinical diagnostics and healthcare. Numerous software systems and tools have been developed to analyze NGS data, and various data formats have been produced to accommodate different sequencing equipment providers or analytical software. However, the data interoperability between these tools brings great challenges to researchers. A generic format that could be shared by most of the software and tools in the NGS field would make data interoperability and sharing easier. In this paper, we defined a general XML-based NGS markup language (NGSML) format for the representation and exchange of NGS data. We also developed a user-friendly GUI tool, NGSMLEditor, for presenting, creating, editing, and converting NGSML files. By using NGSML, various types of NGS data can be saved in one unified format. Compared with the unstructured plain text file, a structured data format based on XML technology solves the incompatibility of various NGS data formats. The NGSML specifications are freely available from http://www.sysbio.org.cn/NGSML. NGSMLEditor is open source under GNU GPL and can be downloaded from the website.

High-Throughput Analysis of CRISPR-Cas9 Editing Outcomes in Cell and Animal Models Using CRIS.py.

Genome editing using the CRISPR-Cas9 platform creates precise modifications in cells and whole organisms. Although knockout (KO) mutations can occur at high frequencies, determining the editing rates in a pool of cells or selecting clones that contain only KO alleles can be a challenge. User-defined knock-in (KI) modifications are achieved at much lower rates, making the identification of correctly modified clones even more challenging. The high-throughput format of targeted next-generation sequencing (NGS) provides a platform allowing sequence information to be gathered from a one to thousands of samples. However, it also poses a challenge in terms of analyzing the large amount of data that is generated. In this chapter, we present and discuss CRIS.py, a simple and highly versatile Python-based program for analyzing NGS data for genome-editing outcomes. CRIS.py can be used to analyze sequencing results for any kind of modification or multiplex modifications specified by the user. Moreover, CRIS.py runs on all fastq files found in a directory, thereby concurrently analyzing all uniquely indexed samples. CRIS.py results are consolidated into two summary files, which allows users to sort and filter results and quickly identify the clones (or animals) of greatest interest.

Systems Biology Analysis of Temporal Dynamics That Govern Endothelial Response to Cyclic Stretch.

Endothelial cells in vivo are subjected to a wide array of mechanical stimuli, such as cyclic stretch. Notably, a 10% stretch is associated with an atheroprotective endothelial phenotype, while a 20% stretch is associated with an atheroprone endothelial phenotype. Here, a systems biology-based approach is used to present a comprehensive overview of the functional responses and molecular regulatory networks that characterize the transition from an atheroprotective to an atheroprone phenotype in response to cyclic stretch. Using primary human umbilical vein endothelial cells (HUVECs), we determined the role of the equibiaxial cyclic stretch in vitro, with changes to the radius of the magnitudes of 10% and 20%, which are representative of physiological and pathological strain, respectively. Following the transcriptome analysis of next-generation sequencing data, we identified four key endothelial responses to pathological cyclic stretch: cell cycle regulation, inflammatory response, fatty acid metabolism, and mTOR signaling, driven by a regulatory network of eight transcription factors. Our study highlights the dynamic regulation of several key stretch-sensitive endothelial functions relevant to the induction of an atheroprone versus an atheroprotective phenotype and lays the foundation for further investigation into the mechanisms governing vascular pathology. This study has significant implications for the development of treatment modalities for vascular disease.