Computer programming is a fundamental tool for life scientists, allowing them to carry out essential research tasks. However, despite various educational efforts, learning to write code can be a challenging endeavor for students and researchers in life-sciences disciplines. Recent advances in artificial intelligence have made it possible to translate human-language prompts to functional code, raising questions about whether these technologies can aid (or replace) life scientists' efforts to write code. Using 184 programming exercises from an introductory-bioinformatics course, we evaluated the extent to which one such tool-OpenAI's ChatGPT-could successfully complete programming tasks. ChatGPT solved 139 (75.5%) of the exercises on its first attempt. For the remaining exercises, we provided natural-language feedback to the model, prompting it to try different approaches. Within 7 or fewer attempts, ChatGPT solved 179 (97.3%) of the exercises. These findings have implications for life-sciences education and research. Instructors may need to adapt their pedagogical approaches and assessment techniques to account for these new capabilities that are available to the general public. For some programming tasks, researchers may be able to work in collaboration with machine-learning models to produce functional code.

MOTIVATION: Orthologous gene identification is fundamental to all aspects of biology. For example, ortholog identification between species can provide functional insights for genes of unknown function and is a necessary step in phylogenetic inference. Currently, most ortholog identification algorithms require all-versus-all BLAST comparisons, which are time-consuming and memory intensive. RESULTS: In contrast to existing approaches, JustOrthologs exploits the conservation of gene structure by using the lengths of coding sequence regions and dinucleotide percentages to identify orthologs. In comparison to OrthoMCL, OMA and OrthoFinder, JustOrthologs decreases ortholog identification runtime by more than 96% and achieves comparable precision and recall scores. The computational speedup allowed us to conduct pairwise comparisons of 1197 complete genomes (780 eukaryotes and 417 archaea). We confirmed gene annotations for 384 120 genes, grouped 1 675 415 genes in previously unreported ortholog groups, and identified 51 429 potentially mislabeled genes across 622 843 ortholog groups. AVAILABILITY AND IMPLEMENTATION: JustOrthologs is an open source collaborative software package available in the GitHub repository: https://github.com/ridgelab/JustOrthologs/. All test FASTA files used for comparisons are freely available at https://github.com/ridgelab/JustOrthologs/comparisonFastaFiles/. Reference genomes used in this work are available for download from the NCBI repository: ftp://ftp.ncbi.nih.gov/genomes/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: One of the main challenges with bioinformatics software is that the size and complexity of datasets necessitate trading speed for accuracy, or completeness. To combat this problem of computational complexity, a plethora of heuristic algorithms have arisen that report a 'good enough' solution to biological questions. However, in instances such as Simple Sequence Repeats (SSRs), a 'good enough' solution may not accurately portray results in population genetics, phylogenetics and forensics, which require accurate SSRs to calculate intra- and inter-species interactions. RESULTS: We present Kmer-SSR, which finds all SSRs faster than most heuristic SSR identification algorithms in a parallelized, easy-to-use manner. The exhaustive Kmer-SSR option has 100% precision and 100% recall and accurately identifies every SSR of any specified length. To identify more biologically pertinent SSRs, we also developed several filters that allow users to easily view a subset of SSRs based on user input. Kmer-SSR, coupled with the filter options, accurately and intuitively identifies SSRs quickly and in a more user-friendly manner than any other SSR identification algorithm. AVAILABILITY AND IMPLEMENTATION: The source code is freely available on GitHub at https://github.com/ridgelab/Kmer-SSR. CONTACT: [email protected].

UNLABELLED: Simple Sequence Repeats (SSRs) are used to address a variety of research questions in a variety of fields (e.g. population genetics, phylogenetics, forensics, etc.), due to their high mutability within and between species. Here, we present an innovative algorithm, SA-SSR, based on suffix and longest common prefix arrays for efficiently detecting SSRs in large sets of sequences. Existing SSR detection applications are hampered by one or more limitations (i.e. speed, accuracy, ease-of-use, etc.). Our algorithm addresses these challenges while being the most comprehensive and correct SSR detection software available. SA-SSR is 100% accurate and detected >1000 more SSRs than the second best algorithm, while offering greater control to the user than any existing software. AVAILABILITY AND IMPLEMENTATION: SA-SSR is freely available at http://github.com/ridgelab/SA-SSR CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Genome-wide association studies (GWAS) have effectively identified genetic factors for many diseases. Many diseases, including Alzheimer's disease (AD), have epistatic causes, requiring more sophisticated analyses to identify groups of variants which together affect phenotype. RESULTS: Based on the GWAS statistical model, we developed a multi-SNP GWAS analysis to identify pairs of variants whose common occurrence signaled the Alzheimer's disease phenotype. CONCLUSIONS: Despite not having sufficient data to demonstrate significance, our preliminary experimentation identified a high correlation between GRIA3 and HLA-DRB5 (an AD gene). GRIA3 has not been previously reported in association with AD, but is known to play a role in learning and memory.

BACKGROUND: Analyzing next-generation sequencing data is difficult because datasets are large, second generation sequencing platforms have high error rates, and because each position in the target genome (exome, transcriptome, etc.) is sequenced multiple times. Given these challenges, numerous bioinformatic algorithms have been developed to analyze these data. These algorithms aim to find an appropriate balance between data loss, errors, analysis time, and memory footprint. Typical analysis pipelines require multiple steps. If one or more of these steps is unnecessary, it would significantly decrease compute time and data manipulation to remove the step. One step in many pipelines is PCR duplicate removal, where PCR duplicates arise from multiple PCR products from the same template molecule binding on the flowcell. These are often removed because there is concern they can lead to false positive variant calls. Picard (MarkDuplicates) and SAMTools (rmdup) are the two main softwares used for PCR duplicate removal. RESULTS: Approximately 92 % of the 17+ million variants called were called whether we removed duplicates with Picard or SAMTools, or left the PCR duplicates in the dataset. There were no significant differences between the unique variant sets when comparing the transition/transversion ratios (p = 1.0), percentage of novel variants (p = 0.99), average population frequencies (p = 0.99), and the percentage of protein-changing variants (p = 1.0). Results were similar for variants in the American College of Medical Genetics genes. Genotype concordance between NGS and SNP chips was above 99 % for all genotype groups (e.g., homozygous reference). CONCLUSIONS: Our results suggest that PCR duplicate removal has minimal effect on the accuracy of subsequent variant calls.

BACKGROUND: Since the advent of next-generation sequencing many previously untestable hypotheses have been realized. Next-generation sequencing has been used for a wide range of studies in diverse fields such as population and medical genetics, phylogenetics, microbiology, and others. However, this novel technology has created unanticipated challenges such as the large numbers of genetic variants. Each caucasian genome has more than four million single nucleotide variants, insertions and deletions, copy number variants, and structural variants. Several formats have been suggested for storing these variants; however, the variant call format (VCF) has become the community standard. RESULTS: We developed new software called the Variant Tool Chest (VTC) to provide much needed tools to work with VCF files. VTC provides a variety of tools for manipulating, comparing, and analyzing VCF files beyond the functionality of existing tools. In addition, VTC was written to be easily extended with new tools. CONCLUSIONS: Variant Tool Chest brings new and important functionality that complements and integrates well with existing software. VTC is available at https://github.com/mebbert/VariantToolChest.

BACKGROUND: The mitochondria are essential organelles and are the location of cellular respiration, which is responsible for the majority of ATP production. Each cell contains multiple mitochondria, and each mitochondrion contains multiple copies of its own circular genome. The ratio of mitochondrial genomes to nuclear genomes is referred to as mitochondrial copy number. Decreases in mitochondrial copy number are known to occur in many tissues as people age, and in certain diseases. The regulation of mitochondrial copy number by nuclear genes has been studied extensively. While mitochondrial variation has been associated with longevity and some of the diseases known to have reduced mitochondrial copy number, the role that the mitochondrial genome itself has in regulating mitochondrial copy number remains poorly understood. RESULTS: We analyzed the complete mitochondrial genomes from 1007 individuals randomly selected from the Cache County Study on Memory Health and Aging utilizing the inferred evolutionary history of the mitochondrial haplotypes present in our dataset to identify sequence variation and mitochondrial haplotypes associated with changes in mitochondrial copy number. Three variants belonging to mitochondrial haplogroups U5A1 and T2 were significantly associated with higher mitochondrial copy number in our dataset. CONCLUSIONS: We identified three variants associated with higher mitochondrial copy number and suggest several hypotheses for how these variants influence mitochondrial copy number by interacting with known regulators of mitochondrial copy number. Our results are the first to report sequence variation in the mitochondrial genome that causes changes in mitochondrial copy number. The identification of these variants that increase mtDNA copy number has important implications in understanding the pathological processes that underlie these phenotypes.

BACKGROUND: Population stratification is a key concern for genetic association analyses. In addition, extreme homogeneity of ethnic origins of a population can make it difficult to interpret how genetic associations in that population may translate into other populations. Here we have evaluated the genetic substructure of samples from the Cache County study relative to the HapMap Reference populations and data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). RESULTS: Our findings show that the Cache County study is similar in ethnic diversity to the self-reported "Whites" in the ADNI sample and less homogenous than the HapMap CEU population. CONCLUSIONS: We conclude that the Cache County study is genetically representative of the general European American population in the USA and is an appropriate population for conducting broadly applicable genetic studies.

DNA sequence alignment is a critical step in identifying homology between organism. The most widely used alignment program, ClustalW is known to suffer from the local minima problem, where suboptimal guide trees produce incorrect gap insertions. The optimization alignment approach, has been shown to be effective in combining alignment and phylogenetic search in order to avoid the problems associated with poor guide trees. The optimization alignment algorithm operates at a small grain size, aligning each tree found, wasting time producing multiple sequence alignments for suboptimal trees. This research develops and analyzes a large grain size algorithm for optimization alignment that iterates through steps of alignment and phylogeny search, thus improving the quality of guide trees used for computation of multiple sequence alignments and eliminating computation of multiple sequence alignments for sub-optimal guide trees. Local minima are avoided by the use of stochastic search methods. Large Grain Size Stochastic Optimization Alignment (LGA) exploits the relationship between phylogenies and multiple sequence alignments, and in so doing achieves improved alignment accuracy. LGA is licensed under the GNU General Public License. Source code and data sets are publicly available at http://csl.cs.byu.edu/lga/