Wet-lab Procedures Research Articles

A program for real-time surveillance of SARS-CoV-2 genetics

The COVID-19 pandemic brought forth an urgent need for widespread genomic surveillance for rapid detection and monitoring of emerging SARS-CoV-2 variants. It necessitated design, development, and deployment of a nationwide infrastructure designed for sequestration, consolidation, and characterization of patient samples that disseminates de-identified information to public authorities in tight turnaround times. Here, we describe our development of such an infrastructure, which sequenced 594,832 high coverage SARS-CoV-2 genomes from isolates we collected in the United States (U.S.) from March 13th 2020 to July 3rd 2023. Our sequencing protocol (‘Virseq’) utilizes wet and dry lab procedures to generate mutation-resistant sequencing of the entire SARS-CoV-2 genome, capturing all major lineages. We also characterize 379 clinically relevant SARS-CoV-2 multi-strain co-infections and ensure robust detection of emerging lineages via simulation. The modular infrastructure, sequencing, and analysis capabilities we describe support the U.S. Centers for Disease Control and Prevention national surveillance program and serve as a model for rapid response to emerging pandemics at a national scale.

The effects of sequencing strategies on Metagenomic pathogen detection using bronchoalveolar lavage fluid samples

ObjectivesMetagenomic next-generation sequencing (mNGS) is a powerful tool for pathogen detection. The accuracy depends on both wet lab and dry lab procedures. The objective of our study was to assess the influence of read length and dataset size on pathogen detection. MethodsIn this study, 43 clinical BALF samples, which tested positive via clinical mNGS and were consistent with the diagnosis, were subjected to re-sequencing on the Illumina NovaSeq 6000 platform. The raw re-sequencing data, consisting of 100 million (M) paired-end 150 bp (PE150) reads, were divided into simulated datasets with eight different data sizes (5 M, 10 M, 15 M, 20 M, 30 M, 50 M, 75 M, 100 M) and five different read lengths (single-end 50 bp (SE50), SE75, SE100, PE100, and PE150). Both Kraken2 and IDseq bioinformatics pipelines were employed to analyze the previously diagnosed pathogens in the simulated data. Detection of pathogens was based on read counts ranging from 1 to 10 and RPM values ranging from 0.2 to 2. ResultsOur results revealed that increasing dataset sizes and read lengths can enhance the performance of mNGS in pathogen detection. However, a larger data sizes for mNGS require higher economic costs and longer turnaround time for data analysis. Our findings indicate 20 M reads being sufficient for SE75 mode to achieve high recall rates. Additionally, high nucleic acid loads in samples can lead to increased stability in pathogen detection efficiency, reducing the impact of sequencing strategies. The choice of bioinformatics pipelines had a significant impact on recall rates achieved in pathogen detection. ConclusionsIncreasing dataset sizes and read lengths can enhance the performance of mNGS in pathogen detection but increase the economic and time costs of sequencing and data analysis. Currently, the 20 M reads in SE75 mode may be the best sequencing option.

Impact of wet-lab protocols on quality of whole-genome short-read sequences from foodborne microbial pathogens

For successful elucidation of a food-borne infection chain, the availability of high-quality sequencing data from suspected microbial contaminants is a prerequisite. Commonly, those investigations are a joint effort undertaken by different laboratories and institutes. To analyze the extent of variability introduced by differing wet-lab procedures on the quality of the sequence data we conducted an interlaboratory study, involving four bacterial pathogens, which account for the majority of food-related bacterial infections: Campylobacter spp., Shiga toxin-producing Escherichia coli, Listeria monocytogenes, and Salmonella enterica. The participants, ranging from German federal research institutes, federal state laboratories to universities and companies, were asked to follow their routine in-house protocols for short-read sequencing of 10 cultures and one isolated bacterial DNA per species. Sequence and assembly quality were then analyzed centrally. Variations within isolate samples were detected with SNP and cgMLST calling. Overall, we found that the quality of Illumina raw sequence data was high with little overall variability, with one exception, attributed to a specific library preparation kit. The variability of Ion Torrent data was higher, independent of the investigated species. For cgMLST and SNP analysis results, we found that technological sequencing artefacts could be reduced by the use of filters, and that SNP analysis was more suited than cgMLST to compare data of different contributors. Regarding the four species, a minority of Campylobacter isolate data showed the in comparison highest divergence with regard to sequence type and cgMLST analysis. We additionally compared the assembler SPAdes and SKESA for their performance on the Illumina data sets of the different species and library preparation methods and found overall similar assembly quality metrics and cgMLST statistics.

Broad Genome Sequencing of Environmental and Clinical Strains and Genotyping.

The Next-Generation Sequencing revolution had a great impact on the genomics of Pseudomonas aeruginosa. Since the first release of the P. aeruginosa PAO1 genome, there are more than 5700 genomes published. This wealth of information has been accompanied by the development of bioinformatic tools for handling genomic and phenotypic data. Bioinformatics, indeed,become de facto a big data science. In this chapter, we give a brief historical overview of the knowledge gained from P. aeruginosa genome sequencing, then we describe the wet-lab procedure to extract the DNA and prepare the library for broad genome sequencing using Illumina MiSeq technology. As last, we describe three user-friendly bioinformatics procedures to infer the P. aeruginosa genotype, starting from NGS data, with the Multi-Locus Sequence Typing method, and visualize it as a minimum spanning tree.

Development and Optimization of an Unbiased, Metagenomics-Based Pathogen Detection Workflow for Infectious Disease and Biosurveillance Applications

Rapid, specific, and sensitive identification of microbial pathogens is critical to infectious disease diagnosis and surveillance. Classical culture-based methods can be applied to a broad range of pathogens but have long turnaround times. Molecular methods, such as PCR, are time-effective but are not comprehensive and may not detect novel strains. Metagenomic shotgun next-generation sequencing (NGS) promises specific identification and characterization of any pathogen (viruses, bacteria, fungi, and protozoa) in a less biased way. Despite its great potential, NGS has yet to be widely adopted by clinical microbiology laboratories due in part to the absence of standardized workflows. Here, we describe a sample-to-answer workflow called PanGIA (Pan-Genomics for Infectious Agents) that includes simplified, standardized wet-lab procedures and data analysis with an easy-to-use bioinformatics tool. PanGIA is an end-to-end, multi-use workflow that can be used for pathogen detection and related applications, such as biosurveillance and biothreat detection. We performed a comprehensive survey and assessment of current, commercially available wet-lab technologies and open-source bioinformatics tools for each workflow component. The workflow includes total nucleic acid extraction from clinical human whole blood and environmental microbial forensic swabs as sample inputs, host nucleic acid depletion, dual DNA and RNA library preparation, shotgun sequencing on an Illumina MiSeq, and sequencing data analysis. The PanGIA workflow can be completed within 24 h and is currently compatible with bacteria and viruses. Here, we present data from the development and application of the clinical and environmental workflows, enabling the specific detection of pathogens associated with bloodstream infections and environmental biosurveillance, without the need for targeted assay development.

Recommendations for the introduction of metagenomic next-generation sequencing in clinical virology, part II: bioinformatic analysis and reporting.

Metagenomic next-generation sequencing (mNGS) is an untargeted technique for determination of microbial DNA/RNA sequences in a variety of sample types from patients with infectious syndromes. mNGS is still in its early stages of broader translation into clinical applications. To further support the development, implementation, optimization and standardization of mNGS procedures for virus diagnostics, the European Society for Clinical Virology (ESCV) Network on Next-Generation Sequencing (ENNGS) has been established. The aim of ENNGS is to bring together professionals involved in mNGS for viral diagnostics to share methodologies and experiences, and to develop application guidelines. Following the ENNGS publication Recommendations for the introduction of mNGS in clinical virology, part I: wet lab procedure in this journal, the current manuscript aims to provide practical recommendations for the bioinformatic analysis of mNGS data and reporting of results to clinicians.

PSpecteR: A User-Friendly and Interactive Application for Visualizing Top-Down and Bottom-Up Proteomics Data in R.

Visual examination of mass spectrometry data is necessary to assess data quality and to facilitate data exploration. Graphics provide the means to evaluate spectral properties, test alternative peptide/protein sequence matches, prepare annotated spectra for publication, and fine-tune parameters during wet lab procedures. Visual inspection of LC-MS data is constrained by proteomics visualization software designed for particular workflows or vendor-specific tools without open-source code. We built PSpecteR, an open-source and interactive R Shiny web application for visualization of LC-MS data, with support for several steps of proteomics data processing, including reading various mass spectrometry files, running open-source database search engines, labeling spectra with fragmentation patterns, testing post-translational modifications, plotting where identified fragments map to reference sequences, and visualizing algorithmic output and metadata. All figures, tables, and spectra are exportable within one easy-to-use graphical user interface. Our current software provides a flexible and modern R framework to support fast implementation of additional features. The open-source code is readily available (https://github.com/EMSL-Computing/PSpecteR), and a PSpecteR Docker container (https://hub.docker.com/r/emslcomputing/pspecter) is available for easy local installation.

Towards a standard protocol in coastal aquaculture biomonitoring: an interlaboratory study to assess reproducibility of the wet lab protocol and of Illumina sequencing

Salmon aquaculture is one of the fastest growing food sectors in the world. Because aquaculture can have a significant impact on the benthic environment, frequent monitoring is required by law. Recently, eDNA (environmental DNA) metabarcoding of benthic bacterial communities emerged as a very powerful tool to infer ecological quality based on amplicon sequence variants (ASVs) and their analyses. To exploit this monitoring approach and enable implementation into routine monitoring programs and official regulations, a high reproducibility of a standardized protocol among different laboratories is a prerequisite. Therefore, sediment samples from a compliance monitoring of a Scottish salmon farm were split into aliquots and distributed to two different laboratories for the wet lab procedure prior to independent Illumina sequencing of the two datasets. We then conducted pattern matching analyses and used supervised machine learning to predict ecological quality for each sample in the two datasets. Results were compared among the two datasets and to the reference data (ecological quality status as obtained from traditional macrofauna-based benthic monitoring). The obtained results demonstrate the high degree of congruence between the ecological quality assessments from the two laboratories. We conclude that the standard wet lab protocol used in our study is sufficiently robust across different laboratories, which meets a milestone criterion for practical routine application in industry.

Recommendations for the introduction of metagenomic high-throughput sequencing in clinical virology, part I: Wet lab procedure.

Metagenomic high-throughput sequencing (mHTS) is a hypothesis-free, universal pathogen detection technique for determination of the DNA/RNA sequences in a variety of sample types and infectious syndromes. mHTS is still in its early stages of translating into clinical application. To support the development, implementation and standardization of mHTS procedures for virus diagnostics, the European Society for Clinical Virology (ESCV) Network on Next-Generation Sequencing (ENNGS) has been established. The aim of ENNGS is to bring together professionals involved in mHTS for viral diagnostics to share methodologies and experiences, and to develop application recommendations. This manuscript aims to provide practical recommendations for the wet lab procedures necessary for implementation of mHTS for virus diagnostics and to give recommendations for development and validation of laboratory methods, including mHTS quality assurance, control and quality assessment protocols.

Technical validation of a high-sensitivity target capture NGS assay using unique molecular identifier approach.

e13657 Background: Identification of a broad spectrum of somatic mutations is crucial to guide targeted therapy such as for non-small cell lung cancer (NSCLC) patients. In the clinical environment, it requires well validated NGS workflow both for the wet-lab and dry-lab procedures. Here we describe a high sensitivity target NGS assay to accurately capture single nucleotide variants (SNVs), short insertions and deletions (indels), copy number alterations and gene rearrangements for formalin-fixed paraffin-embedded (FFPE) NSCLC patient samples. Extensive analytical validation was performed following the checklists of College of American Pathologists. Methods: Next generation sequencing (NGS) libraries were prepared using extracted DNA from FFPE tissue NSCLC patient samples. The protocol for library generation was optimized in several steps and incorporated 10bp unique molecular identifiers (UMIs). The libraries were sequenced on Illumina HiSeq X-Ten platform. The sequence data was analyzed by an in-house bioinformatics pipeline to call somatic mutations at an average depth of 4000X. Results: We tested the accuracy of 68 clinical tumor samples that were also validated by conventional or alternative methods in the third party CAP accredited labs. We observed 100% sensitivity and 100% specificity compared with the other lab¡¯s validation results. To define the limit of detection (LOD) for different mutation types, clinical DNA samples containing different variants were diluted with normal DNA. The LODs for SNV (as in EGFR L858R) and indel (as in EGFR 19del) were 0.5% and 1%, respectively. Addressing the LOD of fusion and copy number alteration is usually challenging. Our NGS assay was able to achieve 2% LOD for gene rearrangement (fusion) and 3.5 copies for copy number amplification. The high reproducibility was also achieved by inter- and intra- replicate experiments. Our NGS assay showed better performance than other widely used commercial NGS assay panels. Conclusions: We have validated an NGS based approach with UMI technology that is able to achieve high accuracy and sensitivity as low as 0.5% for detection of somatic mutations, which will improve the clinical testing performance for NSCLC FFPE samples with low allele frequencies of driver mutations.

Clustering Based Parameter Estimation of Thyroid Hormone Pathway.

An ordinary differential equation (ODE) model of the working of the thyroid system for euthyroidism has been presented. As clinical data for thyroid hormones is relatively scarce, such modelling offers potential benefits over wet lab procedures. Genetic algorithms developed for determining of parameters of the ODE system using the available data have been presented and evaluated. This approach enables subject specific parameter estimation towards characterisation of individual thyroid operation. Initially, a simple steady state model was used. Later a cosinor model for the circadian variation of thyroid hormones was used to obtain more reliable results, as indicated through sensitivity analysis in conjunction with other statistical methods. Our parameter determination method has been tested on groups of patients with similar observed values of thyroid stimulating hormone (TSH), free T 3 and free T 4 (identified through clustering) to determine their parameter values jointly. This approach appears to produce parameter sets with lower variation than parameters determined independently, thus leading to better parameter determination.

Implementing Low-Cost, High Accuracy DNA Barcoding From Single Molecule Sequencing to Screen Larval Tephritid Fruit Flies Intercepted at Ports of Entry

AbstractExotic fruit flies in the family Tephritidae pose a threat to U.S. agriculture and natural resources. As part of ongoing fruit fly detection and exclusion programs, invasive fruit fly adults are periodically trapped in and introduced populations are eradicated from two major fruit producing states: California and Florida. Although the pathways used by flies to enter these states are not clear, one possible introduction pathway is hand-carrying fruits and vegetables infested with eggs or larvae through ports of entry during border crossings or international air travel. These collections represent an important resource for determining what species are most commonly associated with particular ports of entry, their host plants, and source country, making the identification of the intercepted larvae fundamental. Here, we examine cytochrome c oxidase subunit I sequences obtained with Sanger DNA sequencing and Single Molecule Real-time sequencing (SMRT) technologies on a collection of hundreds of intercepted larvae and pupae that are suspected to be fruit pests found in the subfamily Dacinae, in addition to a collection of field-caught flies. Utilizing Pacific Biosciences (PacBio) Circular Consensus Sequencing, we sequenced thousands of independent amplicons in a single SMRT cell. We present our implementation of PacBio’s method by describing wet-lab procedures as well as analytical protocols to automate the process. Finally, for the intercepted material, we compared the generated SMRT data to sequences produced with Sanger and evaluate their quality as a means to identify flies. Using intercepted material, we obtained a sequencing success of over 88% with PacBio’s SMRT sequencing. As the spread of invasive insects grow, so will the size of collections of intercepted material; we found technologies such as SMRT sequencing to be excellent resources to simplify the generation of large amounts of molecular data and to reduce or eliminate all together the manipulation of the sequences.

Bioinformatics and DNA-extraction strategies to reliably detect genetic variants from FFPE breast tissue samples

BackgroundArchived formalin fixed paraffin embedded (FFPE) samples are valuable clinical resources to examine clinically relevant morphology features and also to study genetic changes. However, DNA quality and quantity of FFPE samples are often sub-optimal, and resulting NGS-based genetics variant detections are prone to false positives. Evaluations of wet-lab and bioinformatics approaches are needed to optimize variant detection from FFPE samples.ResultsAs a pilot study, we designed within-subject triplicate samples of DNA derived from paired FFPE and fresh frozen breast tissues to highlight FFPE-specific artifacts. For FFPE samples, we tested two FFPE DNA extraction methods to determine impact of wet-lab procedures on variant calling: QIAGEN QIAamp DNA Mini Kit (“QA”), and QIAGEN GeneRead DNA FFPE Kit (“QGR”). We also used negative-control (NA12891) and positive control samples (Horizon Discovery Reference Standard FFPE). All DNA sample libraries were prepared for NGS according to the QIAseq Human Breast Cancer Targeted DNA Panel protocol and sequenced on the HiSeq 4000. Variant calling and filtering were performed using QIAGEN Gene Globe Data Portal. Detailed variant concordance comparisons and mutational signature analysis were performed to investigate effects of FFPE samples compared to paired fresh frozen samples, along with different DNA extraction methods.In this study, we found that five times or more variants were called with FFPE samples, compared to their paired fresh-frozen tissue samples even after applying molecular barcoding error-correction and default bioinformatics filtering recommended by the vendor. We also found that QGR as an optimized FFPE-DNA extraction approach leads to much fewer discordant variants between paired fresh frozen and FFPE samples. Approximately 92% of the uniquely called FFPE variants were of low allelic frequency range (< 5%), and collectively shared a “C > T|G > A” mutational signature known to be representative of FFPE artifacts resulting from cytosine deamination. Based on control samples and FFPE-frozen replicates, we derived an effective filtering strategy with associated empirical false-discovery estimates.ConclusionsThrough this study, we demonstrated feasibility of calling and filtering genetic variants from FFPE tissue samples using a combined strategy with molecular barcodes, optimized DNA extraction, and bioinformatics methods incorporating genomics context such as mutational signature and variant allelic frequency.

How to integrate wet lab and bioinformatics procedures for wine DNA admixture analysis and compositional profiling: Case studies and perspectives.

The varietal authentication of wines is fundamental for assessing wine quality, and it is part of its compositional profiling. The availability of historical, cultural and chemical composition information is extremely important for quality evaluation. DNA-based techniques are a powerful tool for proving the varietal composition of a wine. SSR-amplification of genomic residual Vitis vinifera DNA, namely Wine DNA Fingerprinting (WDF) is able to produce strong, analytical evidence concerning the monovarietal nature of a wine, and for blended wines by generating the probability of the presence/absence of a certain variety, all in association with a dedicated bioinformatics elaboration of genotypes associated with possible varietal candidates. Together with WDF we could exploit Bioinformatics techniques, due to the number of grape genomes grown. In this paper, the use of WDF and the development of a bioinformatics tool for allelic data validation, retrieved from the amplification of 7 to 10 SSRs markers in the Vitis vinifera genome, are reported. The wines were chosen based on increasing complexity; from monovarietal, experimental ones, to commercial monovarietals, to blended commercial wines. The results demonstrate that WDF, after calculation of different distance matrices and Neighbor-Joining input data, followed by Principal Component Analysis (PCA) can effectively describe the varietal nature of wines. In the unknown blended wines the WDF profiles were compared to possible varietal candidates (Merlot, Pinot Noir, Cabernet Sauvignon and Zinfandel), and the output graphs show the most probable varieties used in the blend as closeness to the tested wine. This pioneering work should be meant as to favor in perspective the multidisciplinary building-up of on-line databanks and bioinformatics toolkits on wine. The paper concludes with a discussion on an integrated decision support system based on bioinformatics, chemistry and cultural data to assess wine quality.

Enabling precision medicine via standard communication of HTS provenance, analysis, and results.

A personalized approach based on a patient's or pathogen’s unique genomic sequence is the foundation of precision medicine. Genomic findings must be robust and reproducible, and experimental data capture should adhere to findable, accessible, interoperable, and reusable (FAIR) guiding principles. Moreover, effective precision medicine requires standardized reporting that extends beyond wet-lab procedures to computational methods. The BioCompute framework (https://w3id.org/biocompute/1.3.0) enables standardized reporting of genomic sequence data provenance, including provenance domain, usability domain, execution domain, verification kit, and error domain. This framework facilitates communication and promotes interoperability. Bioinformatics computation instances that employ the BioCompute framework are easily relayed, repeated if needed, and compared by scientists, regulators, test developers, and clinicians. Easing the burden of performing the aforementioned tasks greatly extends the range of practical application. Large clinical trials, precision medicine, and regulatory submissions require a set of agreed upon standards that ensures efficient communication and documentation of genomic analyses. The BioCompute paradigm and the resulting BioCompute Objects (BCOs) offer that standard and are freely accessible as a GitHub organization (https://github.com/biocompute-objects) following the “Open-Stand.org principles for collaborative open standards development.” With high-throughput sequencing (HTS) studies communicated using a BCO, regulatory agencies (e.g., Food and Drug Administration [FDA]), diagnostic test developers, researchers, and clinicians can expand collaboration to drive innovation in precision medicine, potentially decreasing the time and cost associated with next-generation sequencing workflow exchange, reporting, and regulatory reviews.

Genome sequencing and conservation genomics in the Scandinavian wolverine population.

Genetic approaches have proved valuable to the study and conservation of endangered populations, especially for monitoring programs, and there is potential for further developments in this direction by extending analyses to the genomic level. We assembled the genome of the wolverine (Gulo gulo), a mustelid that in Scandinavia has recently recovered from a significant population decline, and obtained a 2.42Gb draft sequence representing >85% of the genome and including >21,000protein-coding genes. We then performed whole-genome resequencing of 10Scandinavian wolverines for population genomic and demographic analyses. Genetic diversity was among the lowest detected in a red-listed population (mean genome-wide nucleotide diversity of 0.05%). Results of the demographic analyses indicated a long-term decline of the effective population size (Ne ) from 10,000well before the last glaciation to <500 after this period. Current Ne appeared even lower. The genome-wide FIS level was 0.089 (possibly signaling inbreeding), but this effect was not observed when analyzing a set of highly variable SNP markers, illustrating that such markers can give a biased picture of the overall character of genetic diversity. We found significant population structure, which has implications for population connectivity and conservation. We used an integrated microfluidic circuit chip technology to develop an SNP-array consisting of 96 highly informative markers that, together with a multiplex pre-amplification step, was successfully applied to low-quality DNA from scat samples. Our findings will inform management, conservation, and genetic monitoring of wolverines and serve as a genomic roadmap that can be applied to other endangered species. The approach used here can be generally utilized in other systems, but we acknowledge the trade-off between investing in genomic resources and direct conservation actions.

Integrated in silico-in vitro strategy for screening of some traditional Egyptian plants for human aromatase inhibitors

Ethnopharmacological relevanceAromatase enzyme (CYP19) is widely known as a critical target protein for treating hormone-dependent breast cancer. Natural products from traditional medicinal plants continue to be an active source of aromatase inhibitors. Meanwhile, high cost of experimental work and low hit rate associated with HTS have stimulated the implementation of in-silico virtual screening to resolve these pitfalls, where coupling of both classical wet lab procedure and VS may offer a more deepened access to bioactive compounds with less work and time waste. Aim of the studyIn this work, a sequential structure-based and ligand-based virtual screening strategy was utilized for investigating an in-house database of 1720 phytochemical constituents of 29 medicinal plants and natural products used in traditional Egyptian medicine to search for compounds with the potential to be used as inhibitors of the human aromatase enzyme. Materials and methodsThe suggested strategy included using Glide docking with its feature ‘extra precision (XP)’ for carrying out structure-based virtual screening (SBVS) where the resulting hits were further promoted to ligand-based virtual screening (LBVS) through the development of two pharmacophore and QSAR models; one for steroidal and the other for non-steroidal aromatase inhibitors. ResultsThe combined results revealed that Artemisia annua, Zingiber officinale, Cicer arietinum, Annona muricata and Vitex agnus castus were the top scoring plants in terms of in-silico activity scores, respectively. The hydro-alcoholic extracts and different solvent fractions of the top scoring plants were subsequently tested experimentally for their aromatase inhibitory activity, by the aid of in-vitro fluorometric assay. The rank ordering of the activities for the plants agreed with the ordering predicted on the basis of SBVS and LBVS workflow implemented. ConclusionThe suggested strategy provides a reliable means of prospecting in-silico screening of natural products databases in the search for new dug leads as aromatase inhibitors. The hits so obtained can then be subjected to further phytochemical studies, to isolate and identify suitable compounds for further in-vitro testing.

PyAmpli: an amplicon-based variant filter pipeline for targeted resequencing data

BackgroundHaloplex targeted resequencing is a popular method to analyze both germline and somatic variants in gene panels. However, involved wet-lab procedures may introduce false positives that need to be considered in subsequent data-analysis. No variant filtering rationale addressing amplicon enrichment related systematic errors, in the form of an all-in-one package, exists to our knowledge.ResultsWe present pyAmpli, a platform independent parallelized Python package that implements an amplicon-based germline and somatic variant filtering strategy for Haloplex data. pyAmpli can filter variants for systematic errors by user pre-defined criteria. We show that pyAmpli significantly increases specificity, without reducing sensitivity, essential for reporting true positive clinical relevant mutations in gene panel data.ConclusionspyAmpli is an easy-to-use software tool which increases the true positive variant call rate in targeted resequencing data. It specifically reduces errors related to PCR-based enrichment of targeted regions.

Selecting Hypomethylated Genomic Regions Using MRE-Seq.

Here, we describe a method capable of filtering the hypomethylated part of plant genomes, the so-called hypomethylome. The principle of the method is based on the filtration and sequence analysis of small DNA fragments generated by methylation-sensitive four-cutter restriction endonucleases, possessing ((5me))CpG motifs in their recognition sites. The majority of these fragments represent genes and their flanking regions containing also regulatory elements-the gene space of the genome. Besides the enrichment of the gene space, another advantage of the method is the simultaneous depletion of repetitive elements due to their methylated nature and its easy application on complex and large plant genomes. Additionally to the wet lab procedure, we describe how to analyze the data using bioinformatics methods and how to apply the method to comparative studies.

MicroRNA expression profiling in bladder cancer: the challenge of next-generation sequencing in tissues and biofluids.

Bladder cancer (BC) is a heterogeneous disease characterized by a high recurrence rate that necessitates continuous cystoscopic surveillance. MicroRNAs (miRNAs) are detectable in tissues and biofluids such as plasma/serum and urine. They represent promising biomarkers with potential not only for detecting BC but also informing on prognosis and monitoring treatment response. In this review, the many aspects of the application of next-generation sequencing (NGS) to evaluate miRNA expression in BC is discussed, including technical issues as well as a comparison with results obtained by qRT-PCR. The available studies investigating miRNA profiling in BC by NGS are described, with particular attention to the potential applicability on biofluids. Altered miRNA levels have been observed in BC tissues by NGS, but these results so far only partially overlapped among studies and with previous data obtained by qRT-PCR. The discrepancies can be ascribed to the small groups of BC patients sequenced. The few available studies on biofluids are mainly focused on implementing RNA isolation and sequencing workflow. Using NGS to analyze miRNAs in biofluids can potentially provide results comparable to tissues with no invasive procedures for the patients. In particular, the analyses performed on exosomes/microvesicles appear to be more informative. Thanks to the improvement of both wet-lab procedures and pipelines/tools for data analyses, NGS studies on biofluids will be performed on a larger scale. MiRNAs detected in urine and serum/plasma will demonstrate their potentiality to describe the variegated scenario of BC and to become relevant clinical markers.