More Recent Software
SNIPER identifies 3D genome subcompartments by imputing interchromosomal Hi-C contacts
[Xiong and Ma, Nat Commun, 2019]
Phylo-HMRF identifies evolutionary patterns of 3D genome based on multi-species Hi-C data.
[Yang et al. Cell Syst, 2019]
Conic Convolution and DFT Network for classifying microscopy images.
[Chidester et al. ISMB/Bioinformatics, 2019]
A deep learning model to predict enhancer-promoter interactions based on sequence-based features.
[Singh et al. Quantitative Biology, 2019]
Phylo-HMGP identifies evolutionary patterns from multi-species continuous functional genomic signals.
[Yang et al. Cell Syst, 2018]
CTCF-MP predicts if a convergent CTCF motif pair is able to form a chromatin loop using sequence-level features.
[Zhang et al. ISMB/Bioinformatics, 2018]
Implementation and experiments on covergence rates of convolutional sparse dictionary learning.
[Singh et al. AISTATS, 2018]
DBC predicts genomic markers using imaging features.
[Chidester et al. Pac Symp Biocomput, 2018]
PEP predicts enhancer-promoter interactions based on sequence-based features only.
[Yang et al. ISMB/Bioinformatics, 2017]
DESCHRAMBLER probabilistically reconstructs ancestral order of syntenic fragments using chromosome-scale and fragmented genome assemblies.
[Kim et al. PNAS, 2017]
C3 (Cancer Correlation Clustering) identifies cancer mutation patterns from patient cohort by leveraging mutual exclusivity of mutations, patient coverage and driver network concentration principles.
[Hou et al. Bioinformatics, 2016]
LDGM estimates differential network between two tissue types directly without inferring the network for individual tissues.
[Tian et al. Nucleic Acids Res, 2016]
Weaver quantifies allele-specific copy number alteration and allele-specific structural variation in cancer genome using whole-genome sequencing reads.
[Li et al. Cell Syst, 2016]
Previous Software Tools
This model traces the evolution of lineage-specific transcription factor binding sites
without relying on detailed base-by-base cross-species alignments.
[Yokoyama et al. PLOS Comput Biol, 2014]
DawnRank directly prioritizes altered genes on a single patient level
which would allow us to discover potential personalized driver mutations.
[Hou and Ma, Genome Med, 2014]
NCIS (network-assisted co-clustering for the identification of cancer subtypes)
combines molecular interaction network into co-clustering.
[Liu et al. BMC Bioinform, 2014]
PSAR-Align is a multiple sequence realignment tool that can refine a given multiple sequence alignment based on suboptimal alignments.
[Kim and Ma, Bioinformatics, 2013]
RACA (reference-assisted chromosome assembly) is a novel algorithm
to reliably order and orient sequence scaffolds generated by NGS and
assemblers into longer chromosomal fragments.
[Kim et al. PNAS, 2013]
TrueSight utilizes a machine-learning approach to precisely map RNA-seq reads to
splice junctions, combining read mapping quality and coding potential of genomic sequences into a unified model.
[Li et al. Nucleic Acids Res, 2012]
TIGER is a novel de novo genome assembly framework that adapts to available computing
resources by iteratively decomposing the assembly problem into sub-problems.
[Wu et al. BMC Bioinform, 2012]
PSAR is a probabilistic sampling-based alignment reliability score to assess the uncertainty
of multiple sequence alignment. It generates suboptimal alignments based on pair-HMM.
[Kim and Ma, Nucleic Acids Res, 2011]
FusionHunter reliably identifies fusion transcripts in cancer transcriptome based on paired-end RNA-seq.
It detects fusion junctions in base-level resolution.
[Li et al. Bioinformatics, 2011]
Given a set of related genomes, InferCARs reconstructs the chromosomal architecture of their most recent
common ancestor. It also provides lineage-specific genomic breakpoint information.
[Ma et al. Genome Res, 2006]
DupCar reconstructs the ancestral genomic orders with duplications from a set of related genomes (source code available upon request).
[Ma et al. J Comput Biol, 2008]
The program solves the problem of recovering the history of a set of genomes that are related to an unseen common ancestor genome by operations of speciation, deletion, insertion, duplication, and rearrangement (source code available upon request).
[Ma et al. PNAS, 2008]