Clone-array pooled shotgun mapping and sequencing: design and analysis of experiments

Miklós Csürös; Bingshan Li; Aleksandar Milosavljevic

Home/Archive/arXiv

arXiv · q-bio/0312017

Clone-array pooled shotgun mapping and sequencing: design and analysis of experiments

Miklós Csürös·Bingshan Li·Aleksandar Milosavljevic

View original source ↗Read source document ↗Export metadata ↓

Abstract

This paper studies sequencing and mapping methods that rely solely on pooling and shotgun sequencing of clones. First, we scrutinize and improve the recently proposed Clone-Array Pooled Shotgun Sequencing (CAPSS) method, which delivers a BAC-linked assembly of a whole genome sequence. Secondly, we introduce a novel physical mapping method, called Clone-Array Pooled Shotgun Mapping (CAPS-MAP), which computes the physical ordering of BACs in a random library. Both CAPSS and CAPS-MAP construct subclone libraries from pooled genomic BAC clones. We propose algorithmic and experimental improvements that make CAPSS a viable option for sequencing a set of BACs. We provide the first probabilistic model of CAPSS sequencing progress. The model leads to theoretical results supporting previous, less formal arguments on the practicality of CAPSS. We demonstrate the usefulness of CAPS-MAP for clone overlap detection with a probabilistic analysis, and a simulated assembly of the Drosophila melanogaster genome. Our analysis indicates that CAPS-MAP is well-suited for detecting BAC overlaps in a highly redundant library, relying on a low amount of shotgun sequence information. Consequently, it is a practical method for computing the physical ordering of clones in a random library, without requiring additional clone fingerprinting. Since CAPS-MAP requires only shotgun sequence reads, it can be seamlessly incorporated into a sequencing project with almost no experimental overhead.

Explore related subjects

q-bio.GN

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Miklós Csürös, Bingshan Li, Aleksandar Milosavljevic. 2003-12-11. Clone-array pooled shotgun mapping and sequencing: design and analysis of experiments. https://arxiv.org/abs/q-bio/0312017

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

EMMA: an R/Bioconductor package to automate tracking of metadata in functional enrichment analyses

Summary: Functional enrichment analysis (FEA) is a widely used approach for interpreting high-throughput omics data. However, essential methodological details, such as software versions, analysis parameters, and annotation database releases among others, are often incompletely reported, limiting the reproducibility and transparency of enrichment analyses and complicating the assessment of potentially problematic methodological choices. Here we present EMMA, an R/Bioconductor package that integrates with existing FEA tools and automatically captures provenance metadata, such as annotation metadata, software version, and parameters, during the analysis runtime. Our package provides utilities for accessing and exporting the recorded metadata to facilitate transparent reporting and preserve provenance required for reproducible enrichment analyses. This also enables auditing of the results while remaining compatible with existing Bioconductor workflows. Availability and implementation: EMMA is available on Bioconductor under the MIT license (https: //bioconductor.org/packages/EMMA), with its development version also available on GitHub (https: //github.com/imbeimainz/EMMA).

q-bio.GN↗

arXiv2026

Motif-Vocab: StatisticallyCalibrated Transcription-Factor-Identity Tokenization forGenomic Language Models

Tokenization is a central design choice in genomic language models, yet most deoxyribonucleic acid (DNA) tokenizers use characters, fixed-length k-mers, or frequency-derived subwords without explicitly using prior information about the specificity of DNA-binding regulatory factors. We introduce Motif-Vocab, a biologically informed tokenizer that scans both DNA strands for statistically calibrated motif matches, emits transcription-factor (TF) identity tokens, and applies nucleotide, $k$-mer, or byte-pair encoding (BPE) to unmatched sequence. Motif-specific null distributions put position-weight matrices (PWMs) of different lengths and degeneracy on a common significance scale; deterministic overlap rules make the representation reproducible. In controlled Bidirectional Encoder Representations from Transformers (BERT) pretraining on two billion base pairs, real motif libraries outperform randomized-motif controls on 54 of 55 in-scope downstream tasks. On a motif-disjoint recognition task derived from DART-Eval Task 2, TF-specific tokens improve macro-F1 by 0.040 over a position-matched generic motif token and by 0.033 over a matched no-motif tokenizer (95\% bootstrap confidence interval: 0.027--0.038). Motif tokens also receive stronger attribution and produce larger occlusion effects than shuffled controls. Dense no-motif tokenizers remain strong general-purpose baselines, including a near-tie on the five-task BERT-base panel. Thus, Motif-Vocab is not a universal accuracy replacement; it is a targeted, interpretable inductive bias for motif-sensitive genomic modeling.

q-bio.GN↗

arXiv2026

Revolutionizing Genomics with Reinforcement Learning Techniques

In recent years, Reinforcement Learning (RL) has emerged as a powerful tool for solving a wide range of problems, including decision-making and genomics. The exponential growth of raw genomic data over the past two decades has exceeded the capacity of manual analysis, leading to a growing interest in automatic data analysis and processing. RL algorithms are capable of learning from experience with minimal human supervision, making them well-suited for genomic data analysis and interpretation. One of the key benefits of using RL is the reduced cost associated with collecting labeled training data, which is required for supervised learning. While there have been numerous studies examining the applications of Machine Learning (ML) in genomics, this survey focuses exclusively on the use of RL in various genomics research fields, including gene regulatory networks (GRNs), genome assembly, and sequence alignment. We present a comprehensive technical overview of existing studies on the application of RL in genomics, highlighting the strengths and limitations of these approaches. We then discuss potential research directions that are worthy of future exploration, including the development of more sophisticated reward functions as RL heavily depends on the accuracy of the reward function, the integration of RL with other machine learning techniques, and the application of RL to new and emerging areas in genomics research. Finally, we present our findings and conclude by summarizing the current state of the field and the future outlook for RL in genomics.

q-bio.GN↗