The Roots of Bioinformatics

Searls, David B.

doi:10.1371/journal.pcbi.1000809

Cited by 33 publications

(20 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Furthermore, reproducibility can be seen to critically affect various aspects of research, especially in the field of science [29]. However, these days bioinformatics plays a distinct role in many biological and medical studies [64]. Thus, a great effort must be made to make computational research reproducible.…”

Section: Open Sciencementioning

confidence: 99%

Towards reproducible computational drug discovery

et al. 2020

View full text Add to dashboard Cite

The reproducibility of experiments has been a long standing impediment for further scientific progress. Computational methods have been instrumental in drug discovery efforts owing to its multifaceted utilization for data collection, pre-processing, analysis and inference. This article provides an in-depth coverage on the reproducibility of computational drug discovery. This review explores the following topics: (1) the current state-of-the-art on reproducible research, (2) research documentation (e.g. electronic laboratory notebook, Jupyter notebook, etc.), (3) science of reproducible research (i.e. comparison and contrast with related concepts as replicability, reusability and reliability), (4) model development in computational drug discovery, (5) computational issues on model development and deployment, (6) use case scenarios for streamlining the computational drug discovery protocol. In computational disciplines, it has become common practice to share data and programming codes used for numerical calculations as to not only facilitate reproducibility, but also to foster collaborations (i.e. to drive the project further by introducing new ideas, growing the data, augmenting the code, etc.). It is therefore inevitable that the field of computational drug design would adopt an open approach towards the collection, curation and sharing of data/code.

show abstract

Section: Open Sciencementioning

confidence: 99%

Towards reproducible computational drug discovery

et al. 2020

View full text Add to dashboard Cite

show abstract

“…One common opinion draws a distinction between bioinformatics as tool development and computational biology as science [4]. However, no consensus has been reached, nor is it clear whether one is needed.…”

Section: Introductionmentioning

confidence: 99%

A large-scale analysis of bioinformatics code on GitHub

et al. 2018

View full text Add to dashboard Cite

In recent years, the explosion of genomic data and bioinformatic tools has been accompanied by a growing conversation around reproducibility of results and usability of software. However, the actual state of the body of bioinformatics software remains largely unknown. The purpose of this paper is to investigate the state of source code in the bioinformatics community, specifically looking at relationships between code properties, development activity, developer communities, and software impact. To investigate these issues, we curated a list of 1,720 bioinformatics repositories on GitHub through their mention in peer-reviewed bioinformatics articles. Additionally, we included 23 high-profile repositories identified by their popularity in an online bioinformatics forum. We analyzed repository metadata, source code, development activity, and team dynamics using data made available publicly through the GitHub API, as well as article metadata. We found key relationships within our dataset, including: certain scientific topics are associated with more active code development and higher community interest in the repository; most of the code in the main dataset is written in dynamically typed languages, while most of the code in the high-profile set is statically typed; developer team size is associated with community engagement and high-profile repositories have larger teams; the proportion of female contributors decreases for high-profile repositories and with seniority level in author lists; and, multiple measures of project impact are associated with the simple variable of whether the code was modified at all after paper publication. In addition to providing the first large-scale analysis of bioinformatics code to our knowledge, our work will enable future analysis through publicly available data, code, and methods. Code to generate the dataset and reproduce the analysis is provided under the MIT license at https://github.com/pamelarussell/github-bioinformatics. Data are available at https://doi.org/10.17605/OSF.IO/UWHX8.

show abstract

“…Despite linguistic intuitions by Estoup-Zipf, Turing, Shannon and Gamow that now underpin information principles in biology [10][11][12], the core sequence analysis methodologies of sequence alignment and phylogenetics are non-linguistic [13]. However, a linguistic-like alternative -becoming known as alignmentfree methodology -has been developing from attempts to overcome limitations of sequence-based alignment methods in phylogenomics [14][15][16].…”

Section: Genomic Taxonomy Boost By Lexical Clusteringmentioning

confidence: 99%

Genomic Taxonomy Boost by Lexical Clustering

Gramatikoff¹

2014

JIG

View full text Add to dashboard Cite

SummaryMultiple sequence alignment is a foundational technique in bioinformatics, and is often the first step in DNA and protein sequence analyses. However, it can be a slow step for genomic scale datasets, a problem that will only get worse as the sheer scale of biological sequence analyses continues to increase. Sequence alignment is also potentially inappropriate when there have been many small-and large-scale rearrangements among the sequences to be aligned, and subsequent analyses may be sensitive to uncertainties in the alignment. In this paper, we propose an alignment-free methodology for sequence comparison, based on n-gram frequency vectors, and demonstrate its ability to detect ontological relationships in biological literature and DNA sequence families (specifically kinases, Alu repeats and promoter sequences of co-expression networks). The methodology is versatile for clustering methods such as classical hierarchical clustering, as well as non-negative matrix factorization. It is also highly efficient in terms of computational time and space requirements, and we foresee it becoming an indispensable tool in genomic sequence analysis. IntroductionAugust Schleicher, Ernst Haeckel, and other 19 th century AbstractIn the post-genomic era, drawing inferences from multiple massive data sets is a ubiquitous challenge in the computational life sciences. Multiple sequence alignment has played a key role in genomics (and other "omics") as a means of summarizing and representing relationships between sequences. However, two problems with alignment-based strategies are apparent: the computational expense of constructing alignments and the sensitivity of subsequent analyses to alignment uncertainties.Here we present a novel alignment-free alternative. We use frequency profiles (or n-gram vectors) for sequence comparison, a method inspired by lexical statistics. Such profiles can be used to infer relationships between texts or between biological sequences, and we demonstrate that two statistical techniques -hierarchical clustering (HC) and non-negative matrix factorization (NMF) -provide invaluable insights in both contexts.We present four case studies. First, we show that bigram frequency profiles can be used to reconstruct the ontology of 102,402 PubMed titles selected for their relevance to nine drugs and nine therapeutic proteins. Second, we apply the same methodology to classify 63 protein kinase coding DNA sequences into functional categories, based on trigram frequency profiles. The two major classes (Tyr vs Ser/Thr) are correctly identified. Third, and similarly, we show that Alu subfamilies can be identified in 58,122 Alu sequences, in perfect agreement with the accepted topology of the Alu phylogeny, again based only on trigram frequency profiles. Fourth, we clustered 8,885 human promoters using trigram frequency profiles for ab initio discovery of co-expression networks associated with disease.We demonstrate that "lexical" statistics offers a viable alignment-free approach to identifying and representing...

show abstract

The Roots of Bioinformatics

Cited by 33 publications

References 49 publications

Towards reproducible computational drug discovery

Towards reproducible computational drug discovery

A large-scale analysis of bioinformatics code on GitHub

Genomic Taxonomy Boost by Lexical Clustering

Contact Info

Product

Resources

About