The democratization of bioinformatics: A software engineering perspective

Lawlor, Brendan; Sleator, Roy D.

doi:10.1093/gigascience/giaa063

Cited by 7 publications

(6 citation statements)

References 6 publications

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“…Our solution combines a distributed computing engine based on the extended Apache Spark Catalyst query optimizer with the SQL interface for handling large-scale processing and analyzing next generation sequencing datasets in a consistent tabular form. This approach will help to facilitate the adoption of scalable solutions among users that are neither proficient in distributed computing nor in cloud infrastructures as envisioned in Lawlor and Sleator, 2020.…”

Section: Discussionmentioning

confidence: 99%

Cloud-native distributed genomic pileup operations

Wiewiórka

Szmurło

Stankiewicz

et al. 2022

Preprint

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Motivation: Pileup analysis is a building block of many bioinformatics pipelines, including variant calling and genotyping. This step tends to become a bottleneck of the entire assay since the straightforward pileup implementations involve processing of all base calls from all alignments sequentially. On the other hand, a distributed version of the algorithm faces the intrinsic challenge of splitting reads-oriented file formats into self-contained partitions to avoid costly data exchange between computation nodes. Results: Here, we present a scalable, distributed, and efficient implementation of a pileup algorithm that is suitable for deploying in cloud computing environments. In particular, we implemented: (i) our custom data-partitioning algorithm optimized to work with the alignment reads, (ii) a novel and unique approach to process alignment events from sequencing reads using the MD tags, (iii) the source code micro-optimizations for recurrent operations, and (iv) a modular structure of the algorithm. We have proven that our novel approach consistently and significantly outperforms other state-of-the-art distributed tools in terms of execution time (up to 6.5x faster) and memory usage (up to 2x less), resulting in a substantial cloud cost reduction. SeQuiLa is a cloud-native solution that can be easily deployed using any managed Kubernetes and Hadoop services available in public clouds, like Microsoft Azure Cloud, Google Cloud Platform, or Amazon Web Services. Together with the already implemented distributed range joins and coverage calculations, our package provides end-users with an unified SQL interface for convenient analyzing of population-scale genomic data in an interactive way. See https://biodatageeks.github.io/sequila/ for details.

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Section: Discussionmentioning

confidence: 99%

Cloud-native distributed genomic pileup operations

Wiewiórka

Szmurło

Stankiewicz

et al. 2022

Preprint

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“…A poorly documented software may lead the daily work of a researcher to setbacks and delays, by adding a new layer of complexity to the already complex task of working with biological data (Lawlor and Sleator, 2020). It would be similar to conducting a wet lab experiment without fully understanding the chemicals, their activities, or not having the label's information regarding concentration.…”

Section: And the "Ugly" Sidementioning

confidence: 99%

Revealing the expression profile of genes that encode the Subcortical Maternal Complex in human reproductive failures

Rockenbach,

Fraga,

Kowalski

et al. 2023

Genet. Mol. Biol.

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The Subcortical Maternal Complex (SCMC) is composed of maternally encoded proteins required for the early stages of embryo development. Here we aimed to investigate the expression profile of the genes that encode the individual members of the SCMC in human reproductive failures. To accomplish that, we selected three datasets in the Gene Expression Omnibus repository for differential gene expression (DGE) analysis, comprising human endometrial and placental tissues of patients with recurrent implantation failure (RIF) or recurrent pregnancy loss (RPL). The SCMC genes KHDC3L , NLRP2 , NLRP4 , NLRP5 , OOEP , PADI6 , TLE6 , and ZBED3 were included in the DGE analysis, as well as CFL1 and CFL2 that connect the SCMC with the actin cytoskeleton. Additionally, differential co-expression analysis and systems biology analysis of gene-gene co-expression were performed for KHDC3L , NLRP5 , OOEP , and TLE6 , demonstrating gene pairs differentially correlated under the two conditions, and the co-expression with genes involved in immune response, cell cycle, DNA damage repair, embryo development, and male reproduction. Compared to control groups, NLRP5 demonstrated upregulation in the endometrium of RIF patients, and KHDC3L was upregulated in the fetal placental tissue of RPL patients, shedding light on the importance of considering SCMC genes in reproductive failures.

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“…We have already suggested ways in which this can be done. 7 Interactions at team interfaces will be made smoother by scientists following existing advice on good programming practices, 18 and by software engineers adapting to the needs of scientific computing, and providing scientifically useful abstractions over computing systems. 8 …”

Section: Mapping Code To Biologymentioning

confidence: 99%

“…4 One result of this confusion is a difficulty in integrating software engineering skills into bioinformatics, which in turn has negative impacts on research outcomes such as reproducibility, 5 scalability, 6 and productivity. 7,8 One common point of confusion is the implication that the role of software must fit into a fixed hierarchy of value or importance within the life sciences. However, we suggest that this is based on a misunderstanding of the role played by code in any field, not just biology.…”

Section: Introductionmentioning

confidence: 99%

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The roles of code in biology

Lawlor

Sleator

2021

Science Progress

Self Cite

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The way in which computer code is perceived and used in biological research has been a source of some controversy and confusion, and has resulted in sub-optimal outcomes related to reproducibility, scalability and productivity. We suggest that the confusion is due in part to a misunderstanding of the function of code when applied to the life sciences. Code has many roles, and in this paper we present a three-dimensional taxonomy to classify those roles and map them specifically to the life sciences. We identify a “sweet spot” in the taxonomy—a convergence where bioinformaticians should concentrate their efforts in order to derive the most value from the time they spend using code. We suggest the use of the “inverse Conway maneuver” to shape a research team so as to allow dedicated software engineers to interface with researchers working in this “sweet spot.” We conclude that in order to address current issues in the use of software in life science research such as reproducibility and scalability, the field must reevaluate its relationship with software engineering, and adapt its research structures to overcome current issues in bioinformatics such as reproducibility, scalability and productivity.

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The democratization of bioinformatics: A software engineering perspective

Cited by 7 publications

References 6 publications

Cloud-native distributed genomic pileup operations

Cloud-native distributed genomic pileup operations

Revealing the expression profile of genes that encode the Subcortical Maternal Complex in human reproductive failures

The roles of code in biology

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