Microsatellite instability identification software tools | High-throughput sequencing data analysis
Microsatellite instability (MSI) is a form of hypermutation caused by defective DNA mismatch repair (MMR). MSI is characterized by widespread changes in the length of genomic mononucleotide repeats (e.g., AAAAA….) or microsatellites (e.g., GATAGATAGATA….), collectively termed simple repeats. MSI is also characterized by high rates of single-nucleotide-substitution (SNS) mutations. MSI can arise due to germ-line mutations in MMR genes, due to somatic mutations in MMR genes, or due to epigenetic inactivation of MMR genes.
Derives microsatellite instability (MSI) status from tumor-normal paired genome sequencing data. MSIsensor is a program that quantifies MSI in paired tumor-normal genome sequencing data and reports somatic status of corresponding microsatellite sites in the human genome. The software was applied to The Cancer Genome Atlas (TCGA) endometrial exome sequence data from 242 tumor-normal pairs.
Allows users to detect the microsatellite instability (MSI) phenotype using next generation sequencing (NGS). mSINGS permits evaluation of mononucleotide microsatellite loci that were incidentally sequenced after targeted gene enrichment. It can be applied to gene or exome capture panels designed for other purpose.
Allows users to detect microsatellite instability (MSI) in somatic mutation data from whole exome sequencing. MSIseq provides two main functions which implement next-generation sequencing (NGS) classifier. The first one, MSIseq.train(), generates a classifier from training data. The second function classifies tumors using classifiers generated by MSIseq.train(). It also includes a helper function, Compute.input.variables (), to generate the input variables needed by these two functions.
Performs pan-tumor microsatellite instability (MSI) classification from tumor mutation annotation format (MAF) data. MSIpred computes 22 features characterizing tumor somatic mutational load from mutation data generated from paired tumor-normal exome sequencing data, subsequently using these features to predict tumor MSI status with a SVM classifier trained by MAF data of 1074 tumors belonging to four types.
Permits users to detect microsatellite instability in paired tumor-normal patient samples. MANTIS evaluates the general instability present in a tumor sample, using the data from the corresponding normal sample. It allows processing of samples from different sequencing pipelines or tumor types without requiring a different baseline for each. Its performance was analyzed with samples from six cancer types.
Predicts microsatellite instability (MSI) status. MOSAIC is a weighted-tree microsatellite instability classifier that ascertains MSI status from tumor–normal tissue pairs examined with exome-sequencing data. The software was used to assigned MSI status for 5,930 tumor exomes from 18 cancer types. As it corrects for class imbalance in its cross-validation training procedure, MOSAIC allows predictions in new cancer types to be made without prior assumption about the expected prevalence of MSI-H tumors.
Classifies microsatellite instability (MSI) status. MIRMMR trains logistic regression models using DNA methylation and mutation information from mismatch repair (MMR) pathway genes. It reports a score between zero and one, so a suitable cutoff to separate MSI-High samples from Not-MSI-High samples is necessary. This tool offers a promising new option for integrated MSI diagnosis that does not rely on measuring microsatellites.