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More Recent Software

Nucleome Browser

A multimodal, interactive data visualization platform to integrate genomic, imaging, and 3D structure models as well as external data sources to study 4D Nucleome
[Zhu et al. Nat Methods, 2022]


Multiscale and integrative single-cell Hi-C analysis.
[Zhang et al. Nat Biotechnol, 2021]


Dango predicts higher-order genetic interactions.
[Zhang et al. RECOMB, 2021]


SpiceMix learns cell identities from spatial transcriptome data.
[Chidester et al. RECOMB, 2021]


SPIN is an integrative computational method to identify genome-wide chromosome localization patterns relative to multiple nuclear bodies.
[Wang et al. Genome Biol, 2021]


MATCHA enhances data quality of multi-way chromatin interaction data
[Zhang and Ma, Cell Syst, 2020]


MOCHI integrates transcriptional regulation and 3D genome structure
[Tian and Zhang et al. Genome Res, 2020]


Hyper-SAGNN is a self-attention based graph neural network architecture for hypergraphs
[Zhang et al. ICLR, 2020]


MutSpace is a cancer mutation representation learning architecture by large-scale context embedding
[Zhang et al. ISMB, 2020]


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]

Convolutional Sparse Dictionary Learning

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]

Infinite Sites Model

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]