scholarly journals Digestiflow: from BCL to FASTQ with ease

2019 ◽  
Author(s):  
Manuel Holtgrewe ◽  
Mikko Nieminen ◽  
Clemens Messerschmidt ◽  
Dieter Beule
Keyword(s):  
Quality Control ◽  
Software Package ◽  
Flow Cell ◽  
Automated Extraction ◽  
Sequencing Data ◽  
Report Generation ◽  
Raw Data ◽  
Cell Sample ◽  
Cell Data

Management raw sequencing data and its preprocessing (conversion into sequences and demultiplexing) remains a challenging topic for groups running sequencing devices. They face many challenges in such efforts and solutions ranging from manual management of spreadsheets to very complex and customized LIMS systems handling much more than just sequencing raw data. In this manuscript, we describe the software package DigestiFlow that focuses on the management of Illumina flow cell sample sheets and raw data. It allows for automated extraction of information from flow cell data and management of sample sheets. Furthermore, it allows for the automated and reproducible conversion of Illumina base calls to sequences and the demultiplexing thereof using bcl2fastq and Picard Tools, followed by quality control report generation.

scholarly journals Digestiflow: from BCL to FASTQ with ease

2019 ◽  
Author(s):  
Manuel Holtgrewe ◽  
Mikko Nieminen ◽  
Clemens Messerschmidt ◽  
Dieter Beule
Keyword(s):  
Quality Control ◽  
Software Package ◽  
Flow Cell ◽  
Automated Extraction ◽  
Sequencing Data ◽  
Report Generation ◽  
Raw Data ◽  
Cell Sample ◽  
Cell Data

Management raw sequencing data and its preprocessing (conversion into sequences and demultiplexing) remains a challenging topic for groups running sequencing devices. They face many challenges in such efforts and solutions ranging from manual management of spreadsheets to very complex and customized LIMS systems handling much more than just sequencing raw data. In this manuscript, we describe the software package DigestiFlow that focuses on the management of Illumina flow cell sample sheets and raw data. It allows for automated extraction of information from flow cell data and management of sample sheets. Furthermore, it allows for the automated and reproducible conversion of Illumina base calls to sequences and the demultiplexing thereof using bcl2fastq and Picard Tools, followed by quality control report generation.

scholarly journals Digestiflow: from BCL to FASTQ with ease

2019 ◽  
Author(s):  
Manuel Holtgrewe ◽  
Clemens Messerschmidt ◽  
Mikko Nieminen ◽  
Dieter Beule
Keyword(s):  
Software Package ◽  
Flow Cell ◽  
Supplementary Information ◽  
Software Components ◽  
Sequencing Data ◽  
Report Generation ◽  
Raw Data ◽  
Client Software ◽  
Cell Sample ◽  
Cell Data

Abstract Summary Management raw sequencing data and its preprocessing (conversion into sequences and demultiplexing) remains a challenging topic for groups running sequencing devices. They face many challenges in such efforts and solutions ranging from manual management of spreadsheets to very complex and customized LIMS systems handling much more than just sequencing raw data. In this manuscript, we describe the software package DigestiFlow that focuses on the management of Illumina flow cell sample sheets and raw data. It allows for automated extraction of information from flow cell data and management of sample sheets. Furthermore, it allows for the automated and reproducible conversion of Illumina base calls to sequences and the demultiplexing thereof using bcl2fastq and Picard Tools, followed by quality control report generation. Availability and Implementation The software is available under the MIT license at https://github.com/bihealth/digestiflow-server. The client software components are available via Bioconda. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals DigestiFlow - reproducible demultiplexing for the single cell era

2019 ◽  
Author(s):  
Manuel Holtgrewe ◽  
Mikko Nieminen ◽  
Clemens Messerschmidt ◽  
Dieter Beule
Keyword(s):  
Single Cell ◽  
Information Management ◽  
Software Package ◽  
Flow Cell ◽  
Software Components ◽  
Automated Extraction ◽  
Sequencing Data ◽  
Raw Data ◽  
Cell Sample

Summary. Managing raw sequencing data and conversion into sequences (demultiplexing) remains a challenging topic for groups running sequencing devices. They face many challenges in such efforts and solutions range from manual management of spreadsheets to very complex and customized LIMS systems handling much more than just sequencing raw data. In this manuscript, we describe the software package DigestiFlow that focuses on the management of Illumina flow cell sample sheets and raw data. Namely, it allows for automated extraction of flow cell raw data information, management of sample sheets, and the automated (and thus reproducible) demultiplexing of Illumina base calls data. Availability and Implementation. The software is available under the MIT license at https://github.com/bihealth/digestiflow-server. The client and demux software components are available via Bioconda.

484 Bioturing browser: interactively explore public single cell sequencing data

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A520-A520
Author(s):  
Son Pham ◽  
Tri Le ◽  
Tan Phan ◽  
Minh Pham ◽  
Huy Nguyen ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Cell Types ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Data Formats ◽  
Cancer Types ◽  
Cell Data

BackgroundSingle-cell sequencing technology has opened an unprecedented ability to interrogate cancer. It reveals significant insights into the intratumoral heterogeneity, metastasis, therapeutic resistance, which facilitates target discovery and validation in cancer treatment. With rapid advancements in throughput and strategies, a particular immuno-oncology study can produce multi-omics profiles for several thousands of individual cells. This overflow of single-cell data poses formidable challenges, including standardizing data formats across studies, performing reanalysis for individual datasets and meta-analysis.MethodsN/AResultsWe present BioTuring Browser, an interactive platform for accessing and reanalyzing published single-cell omics data. The platform is currently hosting a curated database of more than 10 million cells from 247 projects, covering more than 120 immune cell types and subtypes, and 15 different cancer types. All data are processed and annotated with standardized labels of cell types, diseases, therapeutic responses, etc. to be instantly accessed and explored in a uniform visualization and analytics interface. Based on this massive curated database, BioTuring Browser supports searching similar expression profiles, querying a target across datasets and automatic cell type annotation. The platform supports single-cell RNA-seq, CITE-seq and TCR-seq data. BioTuring Browser is now available for download at www.bioturing.com.ConclusionsN/A

scholarly journals Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities

2009 ◽  
Vol 75 (23) ◽  
pp. 7537-7541 ◽  
Author(s):  
Patrick D. Schloss ◽  
Sarah L. Westcott ◽  
Thomas Ryabin ◽  
Justine R. Hall ◽  
Martin Hartmann ◽  
...  
Keyword(s):  
16S Rrna ◽  
Software Package ◽  
Sequence Data ◽  
Rrna Gene ◽  
Sequencing Data ◽  
Laptop Computer ◽  
Operational Taxonomic Units ◽  
Β Diversity ◽  
Single Piece

ABSTRACT mothur aims to be a comprehensive software package that allows users to use a single piece of software to analyze community sequence data. It builds upon previous tools to provide a flexible and powerful software package for analyzing sequencing data. As a case study, we used mothur to trim, screen, and align sequences; calculate distances; assign sequences to operational taxonomic units; and describe the α and β diversity of eight marine samples previously characterized by pyrosequencing of 16S rRNA gene fragments. This analysis of more than 222,000 sequences was completed in less than 2 h with a laptop computer.

scholarly journals Moving translational mass spectrometry imaging towards transparent and reproducible data analyses: A case study of an urothelial cancer cohort analyzed in the Galaxy framework

2021 ◽  
Author(s):  
Melanie Christine Föll ◽  
Veronika Volkmann ◽  
Kathrin Enderle-Ammour ◽  
Konrad Wilhelm ◽  
Dan Guo ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Quality Control ◽  
Data Analysis ◽  
Mass Spectrometry Imaging ◽  
Urothelial Cancer ◽  
Type I ◽  
Raw Data ◽  
The Galaxy ◽  
Muscle Invasive ◽  
Reproducible Manner

Background: Mass spectrometry imaging (MSI) derives spatial molecular distribution maps directly from clinical tissue specimens. This allows for spatial characterization of molecular compositions of different tissue types and tumor subtypes, which bears great potential for assisting pathologists with diagnostic decisions or personalized treatments. Unfortunately, progress in translational MSI is often hindered by insufficient quality control and lack of reproducible data analysis. Raw data and analysis scripts are rarely publicly shared. Here, we demonstrate the application of the Galaxy MSI tool set for the reproducible analysis of an urothelial carcinoma dataset. Methods: Tryptic peptides were imaged in a cohort of 39 formalin-fixed, paraffin-embedded human urothelial cancer tissue cores with a MALDI-TOF/TOF device. The complete data analysis was performed in a fully transparent and reproducible manner on the European Galaxy Server. Annotations of tumor and stroma were performed by a pathologist and transferred to the MSI data to allow for supervised classifications of tumor vs. stroma tissue areas as well as for muscle-infiltrating and non-muscle invasive urothelial carcinomas. For putative peptide identifications, m/z features were matched to the MSiMass list. Results: Rigorous quality control in combination with careful pre-processing enabled reduction of m/z shifts and intensity batch effects. High classification accuracy was found for both, tumor vs. stroma and muscle-infiltrating vs. non-muscle invasive tumors. Some of the most discriminative m/z features for each condition could be assigned a putative identity: Stromal tissue was characterized by collagen type I peptides and tumor tissue by histone and heat shock protein beta-1 peptides. Intermediate filaments such as cytokeratins and vimentin were discriminative between the tumors with different muscle-infiltration status. To make the study fully reproducible and to advocate the criteria of FAIR (findability, accessibility, interoperability, and reusability) research data, we share the raw data, spectra annotations as well as all Galaxy histories and workflows. Data are available via ProteomeXchange with identifier PXD026459 and Galaxy results via https://github.com/foellmelanie/Bladder_MSI_Manuscript_Galaxy_links. Conclusion: Here, we show that translational MSI data analysis in a fully transparent and reproducible manner is possible and we would like to encourage the community to join our efforts.

scholarly journals Technical Considerations for Reduced Representation Bisulfite Sequencing with Multiplexed Libraries

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Aniruddha Chatterjee ◽  
Euan J. Rodger ◽  
Peter A. Stockwell ◽  
Robert J. Weeks ◽  
Ian M. Morison
Keyword(s):  
Bisulfite Sequencing ◽  
Flow Cell ◽  
Throughput Capacity ◽  
Sequencing Data ◽  
Bioinformatics Pipeline ◽  
Illumina Hiseq ◽  
Reduced Representation ◽  
Reduced Representation Bisulfite Sequencing ◽  
Unique Mapping ◽  
Nucleotide Resolution

Reduced representation bisulfite sequencing (RRBS), which couples bisulfite conversion and next generation sequencing, is an innovative method that specifically enriches genomic regions with a high density of potential methylation sites and enables investigation of DNA methylation at single-nucleotide resolution. Recent advances in the Illumina DNA sample preparation protocol and sequencing technology have vastly improved sequencing throughput capacity. Although the new Illumina technology is now widely used, the unique challenges associated with multiplexed RRBS libraries on this platform have not been previously described. We have made modifications to the RRBS library preparation protocol to sequence multiplexed libraries on a single flow cell lane of the Illumina HiSeq 2000. Furthermore, our analysis incorporates a bioinformatics pipeline specifically designed to process bisulfite-converted sequencing reads and evaluate the output and quality of the sequencing data generated from the multiplexed libraries. We obtained an average of 42 million paired-end reads per sample for each flow-cell lane, with a high unique mapping efficiency to the reference human genome. Here we provide a roadmap of modifications, strategies, and trouble shooting approaches we implemented to optimize sequencing of multiplexed libraries on an a RRBS background.

Quantitative Analysis of Agricultural and Food Products: A New Instrumental and Computerized Approach

1992 ◽  
Vol 75 (1) ◽  
pp. 18-25 ◽  
Author(s):  
Bertrand S Lanher
Keyword(s):  
Fourier Transform ◽  
Quantitative Analysis ◽  
Software Package ◽  
Flow Cell ◽  
Infrared Spectrometry ◽  
Fourier Transform Infrared Spectrometry ◽  
Calibration And Validation ◽  
Study Results ◽  
Validation Standard

Abstract A new software package, ANAQUANT, was specifically designed for a long-term approach to the quantitation of compounds In biological products. Its functionality and validity were tested by measuring fat and protein contents In liquid cow's milk using Fourier transform Infrared spectrometry and a handcrafted transmission flow cell. Calibration and validation standard deviations were 0.2484 and 0.3987 g/kg, respectively, for the prediction of proteins, and 0.3163 and 0.4222 g/L, respectively, for the prediction of butterfat. One month elapsed between calibration of the Instrument and the validation study. Results are consistent with those proposed in the literature.

Variant calling and quality control of large-scale human genome sequencing data

2019 ◽  
Vol 3 (4) ◽  
pp. 399-409 ◽  
Author(s):  
Brandon Jew ◽  
Jae Hoon Sul
Keyword(s):  
Quality Control ◽  
Genome Sequencing ◽  
Genetic Variants ◽  
Large Scale ◽  
Variant Calling ◽  
Sequencing Data ◽  
Computational Approaches ◽  
Sequencing Errors ◽  
Human Genome Sequencing ◽  
Number Of Individuals

Abstract Next-generation sequencing has allowed genetic studies to collect genome sequencing data from a large number of individuals. However, raw sequencing data are not usually interpretable due to fragmentation of the genome and technical biases; therefore, analysis of these data requires many computational approaches. First, for each sequenced individual, sequencing data are aligned and further processed to account for technical biases. Then, variant calling is performed to obtain information on the positions of genetic variants and their corresponding genotypes. Quality control (QC) is applied to identify individuals and genetic variants with sequencing errors. These procedures are necessary to generate accurate variant calls from sequencing data, and many computational approaches have been developed for these tasks. This review will focus on current widely used approaches for variant calling and QC.

scholarly journals gpps: an ILP-based approach for inferring cancer progression with mutation losses from single cell data

2020 ◽  
Vol 21 (S1) ◽  
Author(s):  
Simone Ciccolella ◽  
Mauricio Soto Gomez ◽  
Murray D. Patterson ◽  
Gianluca Della Vedova ◽  
Iman Hajirasouliha ◽  
...  
Keyword(s):  
Single Cell ◽  
Cancer Progression ◽  
False Negative ◽  
Fixed Number ◽  
Phylogeny Reconstruction ◽  
Sequencing Data ◽  
Progression Model ◽  
Great Specificity ◽  
Cell Data ◽  
Tumor Phylogeny

Abstract Background Cancer progression reconstruction is an important development stemming from the phylogenetics field. In this context, the reconstruction of the phylogeny representing the evolutionary history presents some peculiar aspects that depend on the technology used to obtain the data to analyze: Single Cell DNA Sequencing data have great specificity, but are affected by moderate false negative and missing value rates. Moreover, there has been some recent evidence of back mutations in cancer: this phenomenon is currently widely ignored. Results We present a new tool, , that reconstructs a tumor phylogeny from Single Cell Sequencing data, allowing each mutation to be lost at most a fixed number of times. The General Parsimony Phylogeny from Single cell () tool is open source and available at https://github.com/AlgoLab/gpps. Conclusions provides new insights to the analysis of intra-tumor heterogeneity by proposing a new progression model to the field of cancer phylogeny reconstruction on Single Cell data.

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