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Jiajie Zhang

Researcher at Heidelberg Institute for Theoretical Studies

Publications -  10
Citations -  5974

Jiajie Zhang is an academic researcher from Heidelberg Institute for Theoretical Studies. The author has contributed to research in topics: Phylogenetic tree & Markov chain Monte Carlo. The author has an hindex of 8, co-authored 10 publications receiving 4612 citations. Previous affiliations of Jiajie Zhang include King Mongkut's University of Technology Thonburi & Foundation for Research & Technology – Hellas.

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PEAR: a fast and accurate Illumina Paired-End reAd mergeR

TL;DR: The PEAR software for merging raw Illumina paired-end reads from target fragments of varying length evaluates all possible paired- end read overlaps and does not require the target fragment size as input, and implements a statistical test for minimizing false-positive results.
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A general species delimitation method with applications to phylogenetic placements

TL;DR: The Poisson tree processes (PTP) model is introduced to infer putative species boundaries on a given phylogenetic input tree and yields more accurate results than de novo species delimitation methods.
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Multi-rate Poisson tree processes for single-locus species delimitation under maximum likelihood and Markov chain Monte Carlo

TL;DR: The multi‐rate PTP is introduced, an improved method that alleviates the theoretical and technical shortcomings of PTP and consistently yields more accurate delimitations with respect to the taxonomy (i.e., identifies more taxonomic species, infers species numbers closer to theTaxonomy).
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MethyCancer: the database of human DNA methylation and cancer

TL;DR: To study interplay of DNA methylation, gene expression and cancer, a publicly accessible database for human DNA Methylation and Cancer is developed and a powerful search tool is developed to provide user-friendly access to all the data and data connections.
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Phylogeny-aware identification and correction of taxonomically mislabeled sequences

TL;DR: SATIVA is presented, a phylogeny-aware method to automatically identify taxonomically mislabeled sequences (‘mislabels’) using statistical models of evolution and uses the Evolutionary Placement Algorithm (EPA) to detect and score sequences whose taxonomic annotation is not supported by the underlying phylogenetic signal.