A high-throughput pipeline for scalable kit-free RNA extraction.
Ontology highlight
ABSTRACT:
SUBMITTER: Han P
PROVIDER: S-EPMC8636496 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:The pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) has prompted a need for mass testing to identify patients with viral infection. The high demand has created a global bottleneck in testing capacity, which prompted us to modify available resources to extract viral RNA and perform reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) to detect SARS-COV-2. OBJECTIVES:Report on the use of a DNA extraction kit, after modifications, to extract viral RNA that could then be detected using an FDA-approved SARS-COV-2 RT-qPCR assay. MATERIALS AND METHODS:Initially, automated RNA extraction was performed using a modified DNA kit on samples from control subjects, a bacteriophage, and an RNA virus. We then verified the automated extraction using the modified kit to detect in-lab propagated SARSCOV-2 titrations using an FDA approved commercial kit (S, N, and ORF1b genes) and an in-house primer-probe based assay (E, RdRp2 and RdRp4 genes). RESULTS:Automated RNA extraction on serial dilutions SARS-COV-2 achieved successful one-step RT-qPCR detection down to 60 copies using the commercial kit assay and less than 30 copies using the in-house primer-probe assay. Moreover, RT-qPCR detection was successful after automated RNA extraction using this modified protocol on 12 patient samples of SARS-COV-2 collected by nasopharyngeal swabs and stored in viral transport media. CONCLUSIONS:We demonstrated the capacity of a modified DNA extraction kit for automated viral RNA extraction and detection using a platform that is suitable for mass testing. LIMITATIONS:Small patient sample size. CONFLICT OF INTEREST:None.
| S-EPMC7511039 | biostudies-literature
Project description:RNA-seq workflows have become progressively more efficient over time however, RNA extraction still remains a significant bottleneck. For small numbers of sample, highly efficient RNA extraction is simple to perform but becomes costly and laborious at the scale of hundreds of samples. Our prior work has demonstrated that qPCR can be accurately performed using bulk cells samples lysate instead of RNA extraction. We combined this method with the recently developed simple method for rapid RNA-seq library prep; Smart-3SEQ (Foley et al., 2019) and hypothesized that bulk RNA-seq can be performed in a multi-well plate format, and at low cost by performing RNA-seq library prep cDNA synthesis using in-lysate RNA followed by Smart-3SEQ. The result shows success of our approach in various levels: 1) all quality control measures were achieved, 2) Gene expression profiles, gene differential expression and the response patterns reported by in-lysate and purified RNA library highly correlate with each other, and 3) in-lysate RNA-seq library prep performed similar to the gold standard used here (Illumina Truseq) for DEG calling.
2021-01-22 | GSE164650 | GEO
Project description:BACKGROUND:Bioinformatics tools designed to identify lentiviral or retroviral vector insertion sites in the genome of host cells are used to address the safety and long-term efficacy of hematopoietic stem cell gene therapy applications and to study the clonal dynamics of hematopoietic reconstitution. The increasing number of gene therapy clinical trials combined with the increasing amount of Next Generation Sequencing data, aimed at identifying integration sites, require both highly accurate and efficient computational software able to correctly process "big data" in a reasonable computational time. RESULTS:Here we present VISPA2 (Vector Integration Site Parallel Analysis, version 2), the latest optimized computational pipeline for integration site identification and analysis with the following features: (1) the sequence analysis for the integration site processing is fully compliant with paired-end reads and includes a sequence quality filter before and after the alignment on the target genome; (2) an heuristic algorithm to reduce false positive integration sites at nucleotide level to reduce the impact of Polymerase Chain Reaction or trimming/alignment artifacts; (3) a classification and annotation module for integration sites; (4) a user friendly web interface as researcher front-end to perform integration site analyses without computational skills; (5) the time speedup of all steps through parallelization (Hadoop free). CONCLUSIONS:We tested VISPA2 performances using simulated and real datasets of lentiviral vector integration sites, previously obtained from patients enrolled in a hematopoietic stem cell gene therapy clinical trial and compared the results with other preexisting tools for integration site analysis. On the computational side, VISPA2 showed a?>?6-fold speedup and improved precision and recall metrics (1 and 0.97 respectively) compared to previously developed computational pipelines. These performances indicate that VISPA2 is a fast, reliable and user-friendly tool for integration site analysis, which allows gene therapy integration data to be handled in a cost and time effective fashion. Moreover, the web access of VISPA2 ( http://openserver.itb.cnr.it/vispa/ ) ensures accessibility and ease of usage to researches of a complex analytical tool. We released the source code of VISPA2 in a public repository ( https://bitbucket.org/andreacalabria/vispa2 ).
| S-EPMC5702242 | biostudies-literature
Project description:SummaryWe report Spark-based INFERence of the molecular mechanisms of NOn-coding genetic variants (SparkINFERNO), a scalable bioinformatics pipeline characterizing non-coding genome-wide association study (GWAS) association findings. SparkINFERNO prioritizes causal variants underlying GWAS association signals and reports relevant regulatory elements, tissue contexts and plausible target genes they affect. To achieve this, the SparkINFERNO algorithm integrates GWAS summary statistics with large-scale collection of functional genomics datasets spanning enhancer activity, transcription factor binding, expression quantitative trait loci and other functional datasets across more than 400 tissues and cell types. Scalability is achieved by an underlying API implemented using Apache Spark and Giggle-based genomic indexing. We evaluated SparkINFERNO on large GWASs and show that SparkINFERNO is more than 60 times efficient and scales with data size and amount of computational resources.Availability and implementationSparkINFERNO runs on clusters or a single server with Apache Spark environment, and is available at https://bitbucket.org/wanglab-upenn/SparkINFERNO or https://hub.docker.com/r/wanglab/spark-inferno.Contactlswang@pennmedicine.upenn.edu.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC7320617 | biostudies-literature
Project description:RNA sequencing enables high-content/high-complexity measurements in small molecule screens. Whereas the costs of DNA sequencing and RNA-seq library preparation have decreased consistently, RNA extraction remains a significant bottleneck to scalability. We evaluate the performance of a bulk RNA-seq library prep protocol optimized for analysis of many samples of adherent cultured cells in parallel. We combined a low-cost direct lysis buffer compatible with cDNA synthesis (in-lysate cDNA synthesis) with Smart-3SEQ and examine the effects of calmidazolium and fludrocortisone-induced perturbation of primary human dermal fibroblasts. We compared this method to normalized purified RNA inputs from matching samples followed by Smart-3SEQ or Illumina TruSeq library prep. Our results show the minimal effect of RNA loading normalization on data quality, measurement of gene expression patterns, and generation of differentially expressed gene lists. We found that in-lysate cDNA synthesis combined with Smart-3SEQ RNA-seq library prep generated high-quality data with similar ranked DEG lists when compared to library prep with extracted RNA or with Illumina TruSeq. Our data show that small molecule screens or experiments based on many perturbations quantified with RNA-seq are feasible at low reagent and time costs.
| S-EPMC8484438 | biostudies-literature
Project description:BackgroundMany methods have been developed to quantify cell shape in 2D in tissues. For instance, the analysis of epithelial cells in Drosophila embryogenesis or jigsaw puzzle-shaped pavement cells in plant epidermis has led to the development of numerous quantification methods that are applied to 2D images. However, proper extraction of 2D cell contours from 3D confocal stacks for such analysis can be problematic.ResultsWe developed a macro in ImageJ, SurfCut, with the goal to provide a user-friendly pipeline specifically designed to extract epidermal cell contour signals, segment cells in 2D and analyze cell shape. As a reference point, we compared our output to that obtained with MorphoGraphX (MGX). While both methods differ in the approach used to extract the layer of signal, they output comparable results for tissues with shallow curvature, such as pavement cell shape in cotyledon epidermis (as quantified with PaCeQuant). SurfCut was however not appropriate for cell or tissue samples with high curvature, as evidenced by a significant bias in shape and area quantification.ConclusionWe provide a new ImageJ pipeline, SurfCut, that allows the extraction of cell contours from 3D confocal stacks. SurfCut and MGX have complementary advantages: MGX is well suited for curvy samples and more complex analyses, up to computational cell-based modeling on real templates; SurfCut is well suited for rather flat samples, is simple to use, and has the advantage to be easily automated for batch analysis of images in ImageJ. The combination of these two methods thus provides an ideal suite of tools for cell contour extraction in most biological samples, whether 3D precision or high-throughput analysis is the main priority.
| S-EPMC6509810 | biostudies-literature
Project description:Noncoding RNAs (ncRNAs) are important functional RNAs that do not code for proteins. We present a highly efficient computational pipeline for discovering cis-regulatory ncRNA motifs de novo. The pipeline differs from previous methods in that it is structure-oriented, does not require a multiple-sequence alignment as input, and is capable of detecting RNA motifs with low sequence conservation. We also integrate RNA motif prediction with RNA homolog search, which improves the quality of the RNA motifs significantly. Here, we report the results of applying this pipeline to Firmicute bacteria. Our top-ranking motifs include most known Firmicute elements found in the RNA family database (Rfam). Comparing our motif models with Rfam's hand-curated motif models, we achieve high accuracy in both membership prediction and base-pair-level secondary structure prediction (at least 75% average sensitivity and specificity on both tasks). Of the ncRNA candidates not in Rfam, we find compelling evidence that some of them are functional, and analyze several potential ribosomal protein leaders in depth.
| S-EPMC1913097 | biostudies-literature
Project description:Bispecific antibodies (BsAbs) represent an emerging class of immunotherapy, but inefficiency in the current discovery has limited their broad clinical availability. Here we report a high throughput, agnostic, single-cell-based functional screening pipeline, comprising molecular and cell engineering for efficient generation of BsAb library cells, followed by functional interrogation at the single-cell level to identify and sort positive clones and downstream sequence identification and functionality characterization. Using a CD19xCD3 bispecific T cell engager (BiTE) as a model, we demonstrate that our single-cell platform possesses a high throughput screening efficiency of up to one and a half million variant library cells per run and can isolate rare functional clones at a low abundance of 0.008%. Using a complex CD19xCD3 BiTE-expressing cell library with approximately 22,300 unique variants comprising combinatorially varied scFvs, connecting linkers and VL/VH orientations, we have identified 98 unique clones, including extremely rare ones (~ 0.001% abundance). We also discovered BiTEs that exhibit novel properties and insights to design variable preferences for functionality. We expect our single-cell platform to not only increase the discovery efficiency of new immunotherapeutics, but also enable identifying generalizable design principles based on an in-depth understanding of the inter-relationships between sequence, structure, and function.
| S-EPMC10082157 | biostudies-literature
Project description:BackgroundSingle nucleotide polymorphisms (SNPs) as defined here are single base sequence changes or short insertion/deletions between or within individuals of a given species. As a result of their abundance and the availability of high throughput analysis technologies SNP markers have begun to replace other traditional markers such as restriction fragment length polymorphisms (RFLPs), amplified fragment length polymorphisms (AFLPs) and simple sequence repeats (SSRs or microsatellite) markers for fine mapping and association studies in several species. For SNP discovery from chromatogram data, several bioinformatics programs have to be combined to generate an analysis pipeline. Results have to be stored in a relational database to facilitate interrogation through queries or to generate data for further analyses such as determination of linkage disequilibrium and identification of common haplotypes. Although these tasks are routinely performed by several groups, an integrated open source SNP discovery pipeline that can be easily adapted by new groups interested in SNP marker development is currently unavailable.ResultsWe developed SNP-PHAGE (SNP discovery Pipeline with additional features for identification of common haplotypes within a sequence tagged site (Haplotype Analysis) and GenBank (-dbSNP) submissions. This tool was applied for analyzing sequence traces from diverse soybean genotypes to discover over 10,000 SNPs. This package was developed on UNIX/Linux platform, written in Perl and uses a MySQL database. Scripts to generate a user-friendly web interface are also provided with common queries for preliminary data analysis. A machine learning tool developed by this group for increasing the efficiency of SNP discovery is integrated as a part of this package as an optional feature. The SNP-PHAGE package is being made available open source at http://bfgl.anri.barc.usda.gov/ML/snp-phage/.ConclusionSNP-PHAGE provides a bioinformatics solution for high throughput SNP discovery, identification of common haplotypes within an amplicon, and GenBank (dbSNP) submissions. SNP selection and visualization are aided through a user-friendly web interface. This tool is useful for analyzing sequence tagged sites (STSs) of genomic sequences, and this software can serve as a starting point for groups interested in developing SNP markers.
| S-EPMC1626092 | biostudies-literature
Project description:New pipeline for processing stool samples for metaproteomics in a high-throughput and reproducible fashion.
2020-06-12 | PXD017450 | Pride