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http://ki.se/en/meb/twingene-and-genomeeutwin

In collaboration with GenomeEUtwin, the TwinGene project investigates the importance of quantitative trait loci and environmental factors for cardiovascular disease. It is well known that genetic factors are of considerable importance for some familial lipid syndromes and that Type A Behavior pattern and increased lipid levels infer increased risk for cardiovascular disease. It is furthermore known that genetic factors are of importance levels of blood lipid biomarkers. The interplay of genetic and environmental effects for these risk factors in a normal population is less well understood and virtually unknown for the elderly. In the TwinGene project twins born before 1958 are contacted to participate. Health and medication data are collected from self-reported questionnaires, and blood sampling material is mailed to the subject who then contacts a local health care center for blood sampling and a health check-up. In the simple health check-up, height, weight, circumference of waist and hip, and blood pressure are measured. Blood is sampled for DNA extraction, serum collection and clinical chemistry tests of C-reactive protein, total cholesterol, triglycerides, HDL and LDL cholesterol, apolipo��protein A1 and B, glucose and HbA1C. The TwinGene cohort contains more than 10000 of the expected final number of 16000 individuals. Molecular genetic techniques are being used to identify Quantitative Trait Loci (QTLs) for cardiovascular disease and biomarkers in the TwinGene participants. Genome-wide linkage and association studies are ongoing. DZ twins have been genome-scanned with 1000 STS markers and a subset of 300 MZ twins have been genome-scanned with Illumina 317K SNP platform. Association of positional candidate SNPs arising from these genomscans are planned. The TwinGene project is associated with the large European collaboration denoted GenomEUtwin (www.genomeutwin.org, see below) which since 2002 has aimed at gathering genetic data on twins in Europe and setting up the infrastructure needed to enable pooling of data and joint analyses. It has been the funding source for obtaining the genome scan data. Types of samples: * EDTA whole blood * DNA * Serum Number of sample donors: 12 044 (sample collection completed)

Proper citation: KI Biobank - TwinGene (RRID:SCR_006006) Copy   

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https://github.com/adigenova/fast-sg

Algorithm for alignment-free scaffolding graph construction from short or long reads. It allows the reuse of efficient algorithms designed for short read data and permits the definition of novel modular hybrid assembly pipelines.

Proper citation: Fast-SG (RRID:SCR_015934) Copy   

•   Source: SciCrunch Registry
  

https://www.ncbi.nlm.nih.gov/sites/batchentrez

Software program for loading numbers of genome records. Allows the retrieval of a large number of nucleotide sequences or protein sequences, in a batch mode, by importing a file containing a list of the desired GI or accession numbers.

Proper citation: Batch Entrez (RRID:SCR_016634) Copy   

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https://einsteinmed.edu/research/shared-facilities/cores/53/epigenomics/

Part of Einstein Center for Epigenomics and Illumina CSPro (certified service provider) laboratory, offers massively-parallel sequencing (MPS) including fully-automated library preparation, quality control and assurance, and number of assays to study the genome/epigenome. Data analytical services are provided by Computational Genomics Facility.

Proper citation: Albert Einstein College of Medicine Epigenomics Shared Core Facility (RRID:SCR_023284) Copy   

•   Source: SciCrunch Registry
  

https://github.com/huangnengCSU/compleasm

Software genome completeness evaluation tool based on miniprot.

Proper citation: compleasm (RRID:SCR_026370) Copy   

•   Source: SciCrunch Registry
  

https://github.com/YongyiLuo98/BVSim

Software package provides several functions and parameters for simulating genetic variations. Benchmarking variation simulator mimicking human variation spectrum.

Proper citation: BVSim (RRID:SCR_026926) Copy   

•   Source: SciCrunch Registry
  

https://broadinstitute.github.io/warp/docs/Pipelines/SlideTags_Pipeline/README

Software pipeline as open-source, cloud-optimized workflow for processing spatial transcriptomics data. It supports data derived from spatially barcoded sequencing technologies, including Slide-tags-based single-molecule profiling. The pipeline processes raw sequencing data into spatially resolved gene expression matrices, ensuring accurate alignment, spatial positioning, and quantification.

Proper citation: SlideTags.wdl (RRID:SCR_027567) Copy   

•   Source: SciCrunch Registry