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Biomedical subjects

Yun William Yu

Publications and source records attributed to Yun William Yu.

3 recordsLinked to original sources

k-mer-based Upstream Preprocessing of long reads for Isoform Discovery.

Eukaryotic genes can encode multiple protein isoforms based on alternative splicing of their transcribed regions. Most modern novel isoform discovery methods function by identifying and assembling exon splice junctions from an RNA-seq sample. However, splice junctions can only be accurately annotated with time-intensive dynamic programming alignment. This manuscript introduces KuPID, a method for preprocessing long RNA-seq reads with the goal of better identifying novel isoform transcripts. KuPID utilizes k-mer sketching as a prefilter to quickly pseudo-align reads to known reference isoforms. Full alignment need only then be applied to reads that are most relevant to isoform discovery. Not only does KuPID speed up the discovery pipeline, it also increases downstream accuracy by filtering out extraneous reads. KuPID preprocessing simultaneously increases the f1 accuracy of isoform discovery pipelines by up to 11.6 points while decreasing the runtime by a factor of 2-3×;. An optional mode permits a KuPID sample to be paired with both isoform discovery and transcript quantification.

Journal Article

RAmpSim: a thermodynamic simulator for hybridization capture in metagenomic sequencing.

MOTIVATION: Simulators that generate synthetic datasets help address the lack of ground truth for developing and benchmarking computational tools. Many read simulators assume uniform sampling across reference genomes; however, for newer capture-based sequencing technologies (e.g. TELSeq), this assumption is intentionally broken to oversample regions of interest. Along with systematic biases arising from probe multiplicity, sequence composition, and species abundances inherent to capture-based sequencing, this mismatch between modeling assumptions and the characteristics of real data necessitates the design of a new capture-based sequencing-specific simulator. RESULTS: We present RAmpSim, a fast simulator that models bait-target hybridization and fragment capture using a thermodynamic nearest-neighbor energy model and Boltzmann-weighted sampling of binding sites. Fragments are generated through multinomial sampling parameterized by bait concentration, binding energy, and genomic abundance before being passed to existing models of platform-specific errors. Implemented in Rust, RAmpSim reproduces empirical within-genome coverage and cross-species enrichment patterns observed in capture-based metagenomic datasets. RAmpSim generally outperforms a uniform baseline with respect to position-based earth mover's distance when compared against the empirical coverage distribution. Classification analysis also shows high recall in recovering empirical high-coverage regions while outperforming a uniform baseline. AVAILABILITY: Code, example scripts, and data sources are available at https://github.com/az002/RAmpSim.git.

Metagenomics

Incorporating indel channels into average-case analysis of seed-chain-extend.

MOTIVATION: Given a sequence s1 of n letters drawn independently and identically (i.i.d.) from an alphabet of size &#x3c3; and a mutated substring s2 of length m<n, we want to recover the mutation history that generated s2 from s1. Many modern sequence aligners for this task use seed-chain-extend with k-mer seeds. Previously, Shaw and Yu showed linear-gap cost chaining can produce a chain with 1-O(1m) recoverability, the proportion of the mutation history that is recovered, in O(mn2.43&#x3b8;&#x2009;log&#x2009;n) expected time for seed-chain-extend (assuming pre-seeded reference), where &#x3b8;<0.206 is the mutation rate under a substitution-only channel and s1 is uniformly random. A gap remains between theory and practice, as real genomes include insertions and deletions (indels). RESULTS: We introduce mathematical machinery to deal with the two new obstacles introduced by indel channels: the dependence of neighbouring anchors and the presence of anchors that are only partially correct. We prove that expected recoverability of an optimal chain is &#x2265;1-O(1m) and expected runtime is O(mn3.15&#xb7;&#x3b8;T&#x2009;log&#x2009;n), given the total mutation rate &#x3b8;T=&#x3b8;i+&#x3b8;d+&#x3b8;s (sum of substitution, insertion, and deletion rates) is &#x3b8;T&#x2264;0.159. We thus narrow (but not close) the gap between theory and practice. AVAILABILITY AND IMPLEMENTATION: https://github.com/Lazarus42/seed_chainer_indels.

INDEL Mutation