Biology subjects

Chu, G.

Publications and source records attributed to Chu, G..

4 recordsLinked to original sources

bioRxiv2022

Exploring Trade-offs in Scalable Phylogenetic Placement Methods

Phylogenetic placement is the problem of placing "query" sequences into an existing tree (called a "backbone tree"), and is useful in both microbiome analysis and to update large evolutionary trees. The most accurate phylogenetic placement method to date is the maximum likelihood-based method pplacer, which uses RAxML to estimate numeric parameters on the backbone tree and then adds the given query sequence to the edge that maximizes the probability that the resulting tree generates the query sequence. Unfortunately, pplacer fails to return valid outputs on many moderately large datasets, and so is limited to backbone trees with at most [~]10,000 leaves. In TCBB 2022, Wedell et al. introduced SCAMPP, a technique to enable pplacer to run on larger backbone trees. SCAMPP operates by finding a small "placement subtree" specific to each query sequence, within which the query sequence are placed using pplacer. That approach matched the scalability and accuracy of APPLES-2, the previous most scalable method. In this study, we explore a different aspect of pplacers strategy: the technique used to estimate numeric parameters on the backbone tree. We confirm anecdotal evidence that using FastTree instead of RAxML to estimate numeric parameters on the backbone tree enables pplacer to scale to much larger backbone trees, almost (but not quite) matching the scalability of APPLES-2 and pplacer-SCAMPP. We then evaluate the combination of these two techniques - SCAMPP and the use of FastTree. We show that this combined approach, pplacer-SCAMPP-FastTree, has the same scalability as APPLES-2, improves on the scalability of pplacer-FastTree, and achieves better accuracy than the comparably scalable methods. Availability: https://github.com/gillichu/PLUSplacer-taxtastic.

bioinformatics↗

bioRxiv2022

UPP2: Fast and Accurate Alignment Estimation of Datasets with Fragmentary Sequences

MotivationMultiple sequence alignment (MSA) is a basic step in many bioinformatics pipelines. However, achieving highly accurate alignments on large datasets, especially those with sequence length heterogeneity, is a challenging task. UPP (Ultra-large multiple sequence alignment using Phylogeny-aware Profiles) is a method for MSA estimation that builds an ensemble of Hidden Markov Models (eHMM) to represent an estimated alignment on the full length sequences in the input, and then adds the remaining sequences into the alignment using selected HMMs in the ensemble. Although UPP provides good accuracy, it is computationally intensive on large datasets. ResultsWe present UPP2, a direct improvement on UPP. The main advance is a fast technique for selecting HMMs in the ensemble that allows us to achieve the same accuracy as UPP but with greatly reduced runtime. We show UPP2 produces more accurate alignments compared to leading MSA methods on datasets exhibiting substantial sequence length heterogeneity, and is among the most accurate otherwise. Availabilityhttps://github.com/gillichu/sepp Contactwarnow@illinois.edu

bioinformatics↗

bioRxiv2022

User-guided local and global copy-number segmentation for tumor sequencing data

Copy-number aberrations (CNAs) are genetic alterations that amplify or delete the number of copies of large genomic segments. Although they are ubiquitous in cancer and, thus, a critical area of current cancer research, CNA identification from DNA sequencing data is challenging because it requires partitioning of the genome into complex segments with the same copy-number states that may not be contiguous. Existing segmentation algorithms address these challenges either by leveraging the local information among neighboring genomic regions, or by globally grouping genomic regions that are affected by similar CNAs across the entire genome. However, both approaches have limitations: overclustering in the case of local segmentation, or the omission of clusters corresponding to focal CNAs in the case of global segmentation. Importantly, inaccurate segmentation will lead to inaccurate identification of important CNAs. For this reason, most pan-cancer research studies rely on manual procedures of quality control and anomaly correction. To improve copy-number segmentation and their control, we introduce CNAVO_SCPLOWIZC_SCPLOW, a web-based tool that enables the user to simultaneously perform local and global segmentation, thus overcoming the limitations of each approach. Using simulated data, we demonstrate that by several metrics, CNAVO_SCPLOWIZC_SCPLOW allows the user to obtain more accurate segmentation relative to existing local and global segmentation methods. Moreover, we analyze six bulk DNA sequencing samples from three breast cancer patients. By validating with parallel singlecell DNA sequencing data from the same samples, we show that by using CNAVO_SCPLOWIZC_SCPLOW, our user was able to obtain more accurate segmentation and improved accuracy in downstream copy-number calling. CNAVO_SCPLOWIZC_SCPLOW is available at https://github.com/elkebir-group/cnaviz.

genomics↗

bioRxiv2020

Reconstructing the history of founder events using genome-wide patterns of allele sharing across individuals

Founder events play a critical role in shaping genetic diversity, impacting the fitness of a species and disease risk in humans. Yet our understanding of the prevalence and distribution of founder events in humans and other species remains incomplete, as most existing methods for characterizing founder events require large sample sizes or phased genomes. To learn about the frequency and evolutionary history of founder events, we introduce ASCEND (Allele Sharing Correlation for the Estimation of Non-equilibrium Demography), a flexible two-locus method to infer the age and strength of founder events. This method uses the correlation in allele sharing across the genome between pairs of individuals to recover signatures of past bottlenecks. By performing coalescent simulations, we show that ASCEND can reliably estimate the parameters of founder events under a range of demographic scenarios, with genotype or sequence data. We apply ASCEND to ~5,000 worldwide human samples (~3,500 present-day and ~1,500 ancient individuals), and ~1,000 domesticated dog samples. In both species, we find pervasive evidence of founder events in the recent past. In humans, over half of the populations surveyed in our study had evidence for a founder events in the past 10,000 years, associated with geographic isolation, modes of sustenance, and historical invasions and epidemics. We document that island populations have historically maintained lower population sizes than continental groups, ancient hunter-gatherers had stronger founder events than Neolithic Farmers or Steppe Pastoralists, and periods of epidemics such as smallpox were accompanied by major population crashes. Many present-day groups--including Central & South Americans, Oceanians and South Asians--have experienced founder events stronger than estimated in Ashkenazi Jews who have high rates of recessive diseases due to their history of founder events. In dogs, we uncovered extreme founder events in most groups, more than ten times stronger than the median strength of founder events in humans. These founder events occurred during the last 25 generations and are likely related to the establishment of dog breeds during Victorian times. Our results highlight a widespread history of founder events in humans and dogs, and provide insights about the demographic and cultural processes underlying these events.

evolutionary biology↗