Biomedical subjects

Chengpeng Bi

Publications and source records attributed to Chengpeng Bi.

8 recordsLinked to original sources

PubMed2025

Accurate somatic small variant discovery for multiple sequencing technologies with DeepSomatic.

Somatic variant detection is an integral part of cancer genomics analysis. While most methods have focused on short-read sequencing, long-read technologies offer potential advantages in repeat mapping and variant phasing. We present DeepSomatic, a deep-learning method for detecting somatic small nucleotide variations and insertions and deletions from both short-read and long-read data. The method has modes for whole-genome and whole-exome sequencing and can run on tumor-normal, tumor-only and formalin-fixed paraffin-embedded samples. To train DeepSomatic and help address the dearth of publicly available training and benchmarking data for somatic variant detection, we generated and make openly available the Cancer Standards Long-read Evaluation (CASTLE) dataset of six matched tumor-normal cell line pairs whole-genome sequenced with Illumina, PacBio HiFi and Oxford Nanopore Technologies, along with benchmark variant sets. Across samples, both cell line and patient-derived, and across short-read and long-read sequencing technologies, DeepSomatic consistently outperforms existing callers.

Humans↗

PubMed2025

Severus detects somatic structural variation and complex rearrangements in cancer genomes using long-read sequencing.

For the detection of somatic structural variation (SV) in cancer genomes, long-read sequencing is advantageous over short-read sequencing with respect to mappability and variant phasing. However, most current long-read SV detection methods are not developed for the analysis of tumor genomes characterized by complex rearrangements and heterogeneity. Here, we present Severus, a breakpoint graph-based algorithm for somatic SV calling from long-read cancer sequencing. Severus works with matching normal samples, supports unbalanced cancer karyotypes, can characterize complex multibreak SV patterns and produces haplotype-specific calls. On a comprehensive multitechnology cell line panel, Severus consistently outperforms other long-read and short-read methods in terms of SV detection F1 score (harmonic mean of the precision and recall). We also illustrate that compared to long-read methods, short-read sequencing systematically misses certain classes of somatic SVs, such as insertions or clustered rearrangements. We apply Severus to several clinical cases of pediatric leukemia/lymphoma, revealing clinically relevant cryptic rearrangements missed by standard genomic panels.

Humans↗

PubMed2024

DeepSomatic: Accurate somatic small variant discovery for multiple sequencing technologies.

Somatic variant detection is an integral part of cancer genomics analysis. While most methods have focused on short-read sequencing, long-read technologies now offer potential advantages in terms of repeat mapping and variant phasing. We present DeepSomatic, a deep learning method for detecting somatic SNVs and insertions and deletions (indels) from both short-read and long-read data, with modes for whole-genome and exome sequencing, and able to run on tumor-normal, tumor-only, and with FFPE-prepared samples. To help address the dearth of publicly available training and benchmarking data for somatic variant detection, we generated and make openly available a dataset of five matched tumor-normal cell line pairs sequenced with Illumina, PacBio HiFi, and Oxford Nanopore Technologies, along with benchmark variant sets. Across samples and technologies (short-read and long-read), DeepSomatic consistently outperforms existing callers, particularly for indels.

Journal Article↗

PubMed2006

BIPAD: a web server for modeling bipartite sequence elements.

BACKGROUND: Many dimeric protein complexes bind cooperatively to families of bipartite nucleic acid sequence elements, which consist of pairs of conserved half-site sequences separated by intervening distances that vary among individual sites. RESULTS: We introduce the Bipad Server, a web interface to predict sequence elements embedded within unaligned sequences. Either a bipartite model, consisting of a pair of one-block position weight matrices (PWM's) with a gap distribution, or a single PWM matrix for contiguous single block motifs may be produced. The Bipad program performs multiple local alignment by entropy minimization and cyclic refinement using a stochastic greedy search strategy. The best models are refined by maximizing incremental information contents among a set of potential models with varying half site and gap lengths. CONCLUSION: The web service generates information positional weight matrices, identifies binding site motifs, graphically represents the set of discovered elements as a sequence logo, and depicts the gap distribution as a histogram. Server performance was evaluated by generating a collection of bipartite models for distinct DNA binding proteins.

Algorithms↗

PubMed2005

Correlations between scaffold/matrix attachment region (S/MAR) binding activity and DNA duplex destabilization energy.

Scaffold or matrix-attachment regions (S/MARs) are thought to be involved in the organization of eukaryotic chromosomes and in the regulation of several DNA functions. Their characteristics are conserved between plants and humans, and a variety of biological activities have been associated with them. The identification of S/MARs within genomic sequences has proved to be unexpectedly difficult, as they do not appear to have consensus sequences or sequence motifs associated with them. We have shown that S/MARs do share a characteristic structural property, they have a markedly high predicted propensity to undergo strand separation when placed under negative superhelical tension. This result agrees with experimental observations, that S/MARs contain base-unpairing regions (BURs). Here, we perform a quantitative evaluation of the association between the ease of stress-induced DNA duplex destabilization (SIDD) and S/MAR binding activity. We first use synthetic oligomers to investigate how the arrangement of localized unpairing elements within a base-unpairing region affects S/MAR binding. The organizational properties found in this way are applied to the investigation of correlations between specific measures of stress-induced duplex destabilization and the binding properties of naturally occurring S/MARs. For this purpose, we analyze S/MAR and non-S/MAR elements that have been derived from the human genome or from the tobacco genome. We find that S/MARs exhibit long regions of extensive destabilization. Moreover, quantitative measures of the SIDD attributes of these fragments calculated under uniform conditions are found to correlate very highly (r2>0.8) with their experimentally measured S/MAR-binding strengths. These results suggest that duplex destabilization may be involved in the mechanisms by which S/MARs function. They suggest also that SIDD properties may be incorporated into an improved computational strategy to search genomic DNA sequences for sites having the necessary attributes to function as S/MARs, and even to estimate their relative binding strengths.

Antineoplastic Agents↗

PubMed2004

Bipartite pattern discovery by entropy minimization-based multiple local alignment.

Many multimeric transcription factors recognize DNA sequence patterns by cooperatively binding to bipartite elements composed of half sites separated by a flexible spacer. We developed a novel bipartite algorithm, bipartite pattern discovery (Bipad), which produces a mathematical model based on information maximization or Shannon's entropy minimization principle, for discovery of bipartite sequence patterns. Bipad is a C++ program that applies greedy methods to search the bipartite alignment space and examines the upstream or downstream regions of co-regulated genes, looking for cis-regulatory bipartite patterns. An input sequence file with zero or one site per locus is required, and the left and right motif widths and a range of possible gap lengths must be specified. Bipad can run in either single-block or bipartite pattern search modes, and it is capable of comprehensively searching all four orientations of half-site patterns. Simulation studies showed that the accuracy of this motif discovery algorithm depends on sample size and motif conservation level, but results were independent of background composition. Bipad performed equivalent with or better than other pattern search algorithms in correctly identifying Escherichia coli cyclic AMP receptor protein and Bacillus subtilis sigma factor binding site sequences based on experimentally defined benchmarks. Finally, a new bipartite information weight matrix for vitamin D3 receptor/retinoid X receptor alpha (VDR/RXRalpha) binding sites was derived that comprehensively models the natural variability inherent in these sequence elements.

Algorithms↗

PubMed2004

WebSIDD: server for predicting stress-induced duplex destabilized (SIDD) sites in superhelical DNA.

SUMMARY: WebSIDD is a Web-based service designed to predict locations and extents of stress-induced duplex destabilization (SIDD) that occur in a double-stranded DNA molecule of specified base sequence, on which a specified level of superhelical stress is imposed. The algorithm calculates the approximate equilibrium statistical mechanical distribution of a population of identical molecules among its accessible states. The user inputs the DNA sequence, and the program outputs the calculated transition probability and destabilization energy of each base pair in the sequence. As options, the user can specify the temperature and the level of superhelicity. The values of all structural and energy parameters used in the calculation have been experimentally measured. WebSIDD should prove useful for finding SIDD-susceptible sites in genomic sequences, and correlating their occurrence with locations involved in regulatory and pathological processes. This strategy already has illuminated the roles of SIDD in diverse biological regulatory processes, including transcriptional initiation and termination, and the eukaryotic nuclear scaffold attachments that partition chromosomes into domains. AVAILABILITY: http://orange.genomecenter.ucdavis.edu/benham/sidd/index.html

Algorithms↗

PubMed2004

The analysis of stress-induced duplex destabilization in long genomic DNA sequences.

We present a method for calculating predicted locations and extents of stress-induced DNA duplex destabilization (SIDD) as functions of base sequence and stress level in long DNA molecules. The base pair denaturation energies are assigned individually, so the influences of near neighbors, methylated bases, adducts, or lesions can be included. Sample calculations indicate that copolymeric energetics give results that are close to those derived when full near-neighbor energetics are used; small but potentially informative differences occur only in the calculated SIDD properties of moderately destabilized regions. The method presented here for analyzing long sequences calculates the destabilization properties within windows of fixed length N, with successive windows displaced by an offset distance d(o). The final values of the relevant destabilization parameters for each base pair are calculated as weighted averages of the values computed for each window in which that base pair appears. This approach implicitly assumes that the strength of the direct coupling between remote base pairs that is induced by the imposed stress attenuates with their separation distance. This strategy enables calculations of the destabilization properties of DNA sequences of any length, up to and including complete chromosomes. We illustrate its utility by calculating the destabilization properties of the entire E. coli genomic DNA sequence. A preliminary analysis of the results shows that promoters are associated with SIDD regions in a highly statistically significant manner, suggesting that SIDD attributes may prove useful in the computational prediction of promoter locations in prokaryotes.

Base Pairing↗