23,488 research outputs found
A quick guide for student-driven community genome annotation
High quality gene models are necessary to expand the molecular and genetic
tools available for a target organism, but these are available for only a
handful of model organisms that have undergone extensive curation and
experimental validation over the course of many years. The majority of gene
models present in biological databases today have been identified in draft
genome assemblies using automated annotation pipelines that are frequently
based on orthologs from distantly related model organisms. Manual curation is
time consuming and often requires substantial expertise, but is instrumental in
improving gene model structure and identification. Manual annotation may seem
to be a daunting and cost-prohibitive task for small research communities but
involving undergraduates in community genome annotation consortiums can be
mutually beneficial for both education and improved genomic resources. We
outline a workflow for efficient manual annotation driven by a team of
primarily undergraduate annotators. This model can be scaled to large teams and
includes quality control processes through incremental evaluation. Moreover, it
gives students an opportunity to increase their understanding of genome biology
and to participate in scientific research in collaboration with peers and
senior researchers at multiple institutions
Are we there yet? : reliably estimating the completeness of plant genome sequences
Genome sequencing is becoming cheaper and faster thanks to the introduction of next-generation sequencing techniques. Dozens of new plant genome sequences have been released in recent years, ranging from small to gigantic repeat-rich or polyploid genomes. Most genome projects have a dual purpose: delivering a contiguous, complete genome assembly and creating a full catalog of correctly predicted genes. Frequently, the completeness of a species' gene catalog is measured using a set of marker genes that are expected to be present. This expectation can be defined along an evolutionary gradient, ranging from highly conserved genes to species-specific genes. Large-scale population resequencing studies have revealed that gene space is fairly variable even between closely related individuals, which limits the definition of the expected gene space, and, consequently, the accuracy of estimates used to assess genome and gene space completeness. We argue that, based on the desired applications of a genome sequencing project, different completeness scores for the genome assembly and/or gene space should be determined. Using examples from several dicot and monocot genomes, we outline some pitfalls and recommendations regarding methods to estimate completeness during different steps of genome assembly and annotation
HiTRACE: High-throughput robust analysis for capillary electrophoresis
Motivation: Capillary electrophoresis (CE) of nucleic acids is a workhorse
technology underlying high-throughput genome analysis and large-scale chemical
mapping for nucleic acid structural inference. Despite the wide availability of
CE-based instruments, there remain challenges in leveraging their full power
for quantitative analysis of RNA and DNA structure, thermodynamics, and
kinetics. In particular, the slow rate and poor automation of available
analysis tools have bottlenecked a new generation of studies involving hundreds
of CE profiles per experiment.
Results: We propose a computational method called high-throughput robust
analysis for capillary electrophoresis (HiTRACE) to automate the key tasks in
large-scale nucleic acid CE analysis, including the profile alignment that has
heretofore been a rate-limiting step in the highest throughput experiments. We
illustrate the application of HiTRACE on thirteen data sets representing 4
different RNAs, three chemical modification strategies, and up to 480 single
mutant variants; the largest data sets each include 87,360 bands. By applying a
series of robust dynamic programming algorithms, HiTRACE outperforms prior
tools in terms of alignment and fitting quality, as assessed by measures
including the correlation between quantified band intensities between replicate
data sets. Furthermore, while the smallest of these data sets required 7 to 10
hours of manual intervention using prior approaches, HiTRACE quantitation of
even the largest data sets herein was achieved in 3 to 12 minutes. The HiTRACE
method therefore resolves a critical barrier to the efficient and accurate
analysis of nucleic acid structure in experiments involving tens of thousands
of electrophoretic bands.Comment: Revised to include Supplement. Availability: HiTRACE is freely
available for download at http://hitrace.stanford.ed
The Chlamydomonas genome project: A decade on
The green alga Chlamydomonas reinhardtii is a popular unicellular organism for studying photosynthesis, cilia biogenesis, and micronutrient homeostasis. Ten years since its genome project was initiated an iterative process of improvements to the genome and gene predictions has propelled this organism to the forefront of the omics era. Housed at Phytozome, the plant genomics portal of the Joint Genome Institute (JGI), the most up-to-date genomic data include a genome arranged on chromosomes and high-quality gene models with alternative splice forms supported by an abundance of whole transcriptome sequencing (RNA-Seq) data. We present here the past, present, and future of Chlamydomonas genomics. Specifically, we detail progress on genome assembly and gene model refinement, discuss resources for gene annotations, functional predictions, and locus ID mapping between versions and, importantly, outline a standardized framework for naming genes
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