In recent years, adaptive laboratory evolution (ALE) has emerged as a powerful tool for basic research in microbiology (e.g., molecular mechanisms of microbial evolution) and efforts on evolutionary engineering of microbial strains (e.g., accelerated evolution of industrial strains by bringing beneficial mutations). The ongoing rapid development of next-generation sequencing platforms has provided novel insights into growth kinetics and metabolism of microbes, and thus led to great advances of this technique. In this review, we summarize recent advances in the applications of long-term and short-term ALE techniques mainly for microbial strain engineering, and different modes of ALE are also introduced. Furthermore, we discuss the current limitations of ALE and potential solutions. We believe that the information reviewed here will make a significant contribution to further advancement of ALE.
High-Throughput Nucleotide Sequencing ; Laboratories ; Mutation

Objective: To evaluate the impact of the targeted next-generation sequencing (NGS) assay for difficult congenital anemias. Methods: Blood Disease Hospital Anemia Panel 2014 (BDHAP-2014) including 217 known genes of congenital anemias was developed. NGS and parental verification were performed for patients who were suspected diagnosed with congenital anaemia from August 2014 to July 2017. Results: A total of 46 patients were enrolled in this study, the clinical suspection were 11 cases Fanconi anemia (FA), 8 cases congenital dyserythropoietic anemia (CDA), 6 cases congenital sideroblast anemia (CSA), 12 cases congenital hemolytic anemia (CHA), 1 case dyskeratosis congenital (DC), 4 cases iron-refractory iron deficiency anemia and 4 cases unexplained cytopenia (Uc), respectively. 28 (60.9%) of 46 patients became confirmed cases after targeted NGS, corresponding to 44 mutations of which 33 were new. 26(56.5%) patients with results of the assay matching to clinical suspection, including FA (5/11, 45.5%), CSA (6/6, 100.0%), CDA (3/8, 37.5%) and CHA (12/12, 100.0%). 2 (4.3%) cases not matching to clinical suspection, including dyskeratosis congenital (DC) was made in 1(2.2%) patients with suspected FA and familial hemophagocytic lymphohistiocytosis (FHL) was made in 1(2.2%) patients with suspected unexplained cytopenia (Uc). In 12 CHA patients, the hemolytic type was further clarified by the NGS. The remaining 18 cases were not clearly diagnosed. Conclusion: Targeted NGS assay is of major impact on congenital anemias. The assay should be used routinely in congenital anemias.
Anemia ; High-Throughput Nucleotide Sequencing ; Humans

The development of high-throughput sequencing techniques enabled a deeper and more comprehensive understanding of environmental microbiology. Specifically, the third-generation sequencing techniques represented by nanopore sequencing have greatly promoted the development of environmental microbiology research due to its advantages such as long sequencing reads, fast sequencing speed, real-time monitoring of sequencing data, and convenient machine carrying, as well as no GC bias and no PCR amplification requirement. This review briefly summarized the technical principle and characteristics of nanopore sequencing, followed by discussing the application of nanopore sequencing techniques in the amplicon sequencing, metagenome sequencing and whole genome sequencing of environmental microorganisms. The advantages and challenges of nanopore sequencing in the application of environmental microbiology research were also analyzed.
Environmental Microbiology ; High-Throughput Nucleotide Sequencing ; Metagenome ; Nanopore Sequencing ; Nanopores

OBJECTIVETo establish a hyperphenylalaninemia related genes screening method using Ion Torrent Personal Genome Machine (PGM) for early detection and differential diagnosis of hyperphenylalaninemia (HPA).

METHODSThree children with known HPA mutations and a healthy control were used for setting up the method. Ten children with HPA with known mutations were recruited for validating the method. Ion Ampliseq PCR was used to amplify the 5' and 3' untranslated region, coding sequence, and flanking introns of PAH, GCH1, PTS, QDPR, and PCBD1 genes. After the enrichment with the Ion OneTouch system, the products were sequenced by PGM. Data from the PGM were processed with Torrent Suite v2.2 software package. All variations were confirmed by Sanger sequencing.

RESULTSFor the 4 samples, the PGM output was 94.22 Mb, with approximately 99.5% of reads mapping to the target regions. Among these samples, we detected 74 variations (28 positions) including 6 known mutations. Compared with database and results of Sanger sequencing, 55 (18 positions) polymorphisms and 13 (4 positions) false positive calls were confirmed. For the 10 samples, all the known mutations were successfully identified.

CONCLUSIONIon Torrent PGM sequencing is suitable for screening genetic mutation underlying HPA from the perspective of metabolic pathways, which can meet the clinical demand for individualized diagnosis and treatment.

The big data from high throughput research disclosed 4V features: volume of data, variety of data, value for deep mining, and velocity of processing speed. Regarding the whole genome sequencing for human sample, at average 30x of coverage, a total of 100 GB of original data (compression FASTQ format) could be produced. Replying to the binary BAM format, a total of 150 GB data could be produced. In the analysis of high throughput data, we need to combine both clinical information and pathological features. In addition, the data sources of medical research involved in ethical and privacy of patients. At present, the costs are gradually cheaper. For example, a whole genome sequencing by Illumina X Ten with 30x coverage costs about 10,000 RMB, and RNA-seq costs 5000 RMB for a single sample. Therefore, cancer genome research provides opportunities for discovery of molecular targets, but also brings enormous challenges on the data integration and utilization. This article introduces methodologies for high throughput data analysis and processing, and explains possible application on molecular target discovery.
Computational Biology ; High-Throughput Nucleotide Sequencing ; Humans ; Neoplasms

We examine the role of big data and machine learning in cancer research. We describe an example in cancer research where gene-level data from The Cancer Genome Atlas (TCGA) consortium is interpreted using a pathway-level model. As the complexity of computational models increases, their sample requirements grow exponentially. This growth stems from the fact that the number of combinations of variables grows exponentially as the number of variables increases. Thus, a large sample size is needed. The number of variables in a computational model can be reduced by incorporating biological knowledge. One particularly successful way of doing this is by using available gene regulatory, signaling, metabolic, or context-specific pathway information. We conclude that the incorporation of existing biological knowledge is essential for the progress in using big data for cancer research.
Computer Simulation ; Genome ; High-Throughput Nucleotide Sequencing ; Humans ; Neoplasms

High-Throughput Nucleotide Sequencing ; Humans ; Rare Diseases ; genetics

Hepatitis B virus ; classification ; genetics ; High-Throughput Nucleotide Sequencing

PURPOSE: Transition to next generation sequencing (NGS) for BRCA1/BRCA2 analysis in clinical laboratories is ongoing but different platforms and/or data analysis pipelines give different results resulting in difficulties in implementation. We have evaluated the Ion Personal Genome Machine (PGM) Platforms (Ion PGM, Ion PGM Dx, Thermo Fisher Scientific) for the analysis of BRCA1/2. MATERIALS AND METHODS: The results of Ion PGM with OTG-snpcaller, a pipeline based on Torrent mapping alignment program and Genome Analysis Toolkit, from 75 clinical samples and 14 reference DNA samples were compared with Sanger sequencing for BRCA1/BRCA2. Ten clinical samples and 14 reference DNA samples were additionally sequenced by Ion PGM Dx with Torrent Suite. RESULTS: Fifty types of variants including 18 pathogenic or variants of unknown significance were identified from 75 clinical samples and known variants of the reference samples were confirmed by Sanger sequencing and/or NGS. One false-negative results were present for Ion PGM/OTG-snpcaller for an indel variant misidentified as a single nucleotide variant. However, eight discordant results were present for Ion PGM Dx/Torrent Suite with both false-positive and -negative results. A 40-bp deletion, a 4-bp deletion and a 1-bp deletion variant was not called and a false-positive deletion was identified. Four other variants were misidentified as another variant. CONCLUSION: Ion PGM/OTG-snpcaller showed acceptable performance with good concordance with Sanger sequencing. However, Ion PGM Dx/Torrent Suite showed many discrepant results not suitable for use in a clinical laboratory, requiring further optimization of the data analysis for calling variants.
DNA ; Genome ; High-Throughput Nucleotide Sequencing ; Humans ; Statistics as Topic

Next generation sequencing (NGS), a high-throughput DNA sequencing technology, is widely used for molecular biological studies. In NGS, RNA-sequencing (RNA-Seq), which is a short-read massively parallel sequencing, is a major quantitative transcriptome tool for different transcriptome studies. To utilize the RNA-Seq data, various quantification and analysis methods have been developed to solve specific research goals, including identification of differentially expressed genes and detection of novel transcripts. Because of the accumulation of RNA-Seq data in the public databases, there is a demand for integrative analysis. However, the available RNA-Seq data are stored in different formats such as read count, transcripts per million, and fragments per kilobase million. This hinders the integrative analysis of the RNA-Seq data. To solve this problem, we have developed a web-based application using Shiny, COEX-seq (Convert a Variety of Measurements of Gene Expression in RNA-Seq) that easily converts data in a variety of measurement formats of gene expression used in most bioinformatic tools for RNA-Seq. It provides a workflow that includes loading data set, selecting measurement formats of gene expression, and identifying gene names. COEX-seq is freely available for academic purposes and can be run on Windows, Mac OS, and Linux operating systems. Source code, sample data sets, and supplementary documentation are available as well.
Computational Biology ; Dataset ; Gene Expression ; High-Throughput Nucleotide Sequencing ; Transcriptome