17 skills found
mikolmogorov / FlyeDe novo assembler for single molecule sequencing reads using repeat graphs
aquaskyline / ClairvoyanteClairvoyante: a multi-task convolutional deep neural network for variant calling in Single Molecule Sequencing
xjtu-omics / SVisionDetecting genome structural variants with deep learning in single molecule sequencing
xiaochuanle / MECATMECAT: an ultra-fast mapping, error correction and de novo assembly tool for single-molecule sequencing reads
vpc-ccg / LordfastSensitive and Fast Alignment Search Tool for Long Read sequencing Data.
PacificBiosciences / KineticsToolsTools for detecting DNA modifications from single molecule, real-time sequencing data
shizhuoxing / ScISA Toolssingle-cell-Isoform-Sequencing-Analysis-Tools: New and powerful tools brings single-cell RNA sequencing to the Isoform level and single molecule resolution.
shizhuoxing / BGI Full Length RNA Analysis PipelineFull-Length RNA Analysis pipeline developted by BGI RD group.
weir12 / DeepEditDeepEdit: single-molecule detection and phasing of A-to-I RNA editing events using Nanopore direct RNA sequencing
CSB5 / INC SeqINC-Seq: Accurate single molecule reads using nanopore sequencing
LankyCyril / EdgecaseA framework for extracting telomeric reads from single-molecule sequencing experiments, describing their sequence variation and motifs, and for haplotype inference.
physnano / RRNA NanoSHAPECode accompanying "Direct detection of RNA modifications and structure using single molecule nanopore sequencing"
HKU-BAL / TranslocatorTranslocator: local realignment and global remapping enabling accurate translocation detection using single-molecule sequencing long reads
maiziezhoulab / VolcanoSVVolcanoSV enables accurate and robust structural variant calling in diploid genomes from single-molecule long read sequencing
Theo-Nelson / SMS ColabPhD-Level Course for Single-Molecule Sequencing Technologies
Samudraneel-98 / Cancer Subtype Prediction Using Multi Omics DatasetImportance of Cancer Subtype prediction: Cancer is a heterogeneous disease caused by chemical, physical, or genetic factors. Identification of cancer subtypes is of great importance to facilitate cancer diagnosis and therapy. Bioinformatics approaches have gradually taken the place of clinical observations and pathological experiments. The development of high-throughput genome analysis techniques on the research of cancer subtypes plays an important role in the analysis and clinical treatment of various kinds of cancers. Omics dataset: The process of mapping and sequencing the human genome began, new technologies have made it possible to obtain a huge number of molecular measurements within a tissue or cell. These technologies can be applied to a biological system of interest to obtain a snapshot of the underlying biology at a resolution that has never before been possible. Broadly speaking, the scientific fields associated with measuring such biological molecules in a high-throughput way are called omics.Omics are novel, comprehensive approaches for analysis of complete genetic or molecular profiles of humans and other organisms. the types of omics data that can be used to develop an omics-based test are discussed below: genomics, proteomics, transcriptomics and metabolomics. Importance of Omics Data with respect to Cancer Prediction: Accurately predicting cancer prognosis is necessary to choose precise strategies of treatment for patients. One of effective approaches in the prediction is the integration of multi-omics data, which reduces the impact of noise within single omics data. A number of methods have been proposed to integrate multi-sources data to identify cancer subtypes in recent years.Based on these types of expression data, various computational methods have been proposed to predict cancer subtypes. It is crucial to study how to better integrate these multiple profiles of data. Approaches of omics data concatenation: 1.Integrative NMF 2.Similarity Network Fusion 3.Joint Non Negative Matrix Factorization Deep Learning: Deep learning is an artificial intelligence (AI) function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Hyperparameter tuning: Hyperparameter tuning works by running multiple trials in a single training job. Each trial is a complete execution of your training application with values for your chosen hyperparameters, set within limits you specify. The AI Platform Training training service keeps track of the results of each trial and makes adjustments for subsequent trials. When the job is finished,you can get a summary of all the trials along with the most effective configuration of values according to the criteria you specify.
fanglab / SMRTERSingle Molecule Real Time sequencing Epigenetic Refinement
