Combining iCn3D and NextStrain to create a novel undergraduate research experience around SARS-CoV-2 variants and commercial antibodies.

Undergraduate research experiences are increasingly important in biology education with efforts underway to provide more projects by embedded them in a course. The shift to online learning at the beginning of the pandemic presented a challenge. How could biology instructors provide research experiences to students who were unable to attend in-person labs? During the 2021 ISMB (Intelligent Systems for Molecular Biology) iCn3D Hackathon-Collaborative Tools for Protein Analysis-we learned about new capabilities in iCn3D for analyzing the interactions between amino acids in the paratopes of antibodies with amino acids in the epitopes of antigens and predicting the effects of mutations on binding. Additionally, new sequence alignment tools in iCn3D support aligning protein sequences with sequences in structure models. We used these methods to create a new undergraduate research project, that students could perform online as part of a course, by combining the use of new features in iCn3D with analysis tools in NextStrain, and a data set of anti-SARS-CoV-2 antibodies. We present results from an example project to illustrate how students would investigate the likelihood of SARS-CoV-2 variants escaping from commercial antibodies and use chemical interaction data to support their hypotheses. We also demonstrate that online tools (iCn3D, NextStrain, and the NCBI databases) can be used to carry out the necessary steps and that this work satisfies the requirements for course-based undergraduate research. This project reinforces major concepts in undergraduate biology-evolution and the relationship between the sequence of a protein, its three-dimensional structure, and its function.

Barriers to integration of bioinformatics into undergraduate life sciences education: A national study of US life sciences faculty uncover significant barriers to integrating bioinformatics into undergraduate instruction.

Bioinformatics, a discipline that combines aspects of biology, statistics, mathematics, and computer science, is becoming increasingly important for biological research. However, bioinformatics instruction is not yet generally integrated into undergraduate life sciences curricula. To understand why we studied how bioinformatics is being included in biology education in the US by conducting a nationwide survey of faculty at two- and four-year institutions. The survey asked several open-ended questions that probed barriers to integration, the answers to which were analyzed using a mixed-methods approach. The barrier most frequently reported by the 1,260 respondents was lack of faculty expertise/training, but other deterrents-lack of student interest, overly-full curricula, and lack of student preparation-were also common. Interestingly, the barriers faculty face depended strongly on whether they are members of an underrepresented group and on the Carnegie Classification of their home institution. We were surprised to discover that the cohort of faculty who were awarded their terminal degree most recently reported the most preparation in bioinformatics but teach it at the lowest rate.

Bioinformatics core competencies for undergraduate life sciences education.

Although bioinformatics is becoming increasingly central to research in the life sciences, bioinformatics skills and knowledge are not well integrated into undergraduate biology education. This curricular gap prevents biology students from harnessing the full potential of their education, limiting their career opportunities and slowing research innovation. To advance the integration of bioinformatics into life sciences education, a framework of core bioinformatics competencies is needed. To that end, we here report the results of a survey of biology faculty in the United States about teaching bioinformatics to undergraduate life scientists. Responses were received from 1,260 faculty representing institutions in all fifty states with a combined capacity to educate hundreds of thousands of students every year. Results indicate strong, widespread agreement that bioinformatics knowledge and skills are critical for undergraduate life scientists as well as considerable agreement about which skills are necessary. Perceptions of the importance of some skills varied with the respondent's degree of training, time since degree earned, and/or the Carnegie Classification of the respondent's institution. To assess which skills are currently being taught, we analyzed syllabi of courses with bioinformatics content submitted by survey respondents. Finally, we used the survey results, the analysis of the syllabi, and our collective research and teaching expertise to develop a set of bioinformatics core competencies for undergraduate biology students. These core competencies are intended to serve as a guide for institutions as they work to integrate bioinformatics into their life sciences curricula.

Characterizing multi-omic data in systems biology.

In today's biology, studies have shifted to analyzing systems over discrete biochemical reactions and pathways. These studies depend on combining the results from scores of experimental methods that analyze DNA; mRNA; noncoding RNAs, DNA, RNA, and protein interactions; and the nucleotide modifications that form the epigenome into global datasets that represent a diverse array of "omics" data (transcriptional, epigenetic, proteomic, metabolomic). The methods used to collect these data consist of high-throughput data generation platforms that include high-content screening, imaging, flow cytometry, mass spectrometry, and nucleic acid sequencing. Of these, the next-generation DNA sequencing platforms predominate because they provide an inexpensive and scalable way to quickly interrogate the molecular changes at the genetic, epigenetic, and transcriptional level. Furthermore, existing and developing single-molecule sequencing platforms will likely make direct RNA and protein measurements possible, thus increasing the specificity of current assays and making it possible to better characterize "epi-alterations" that occur in the epigenome and epitranscriptome. These diverse data types present us with the largest challenge: how do we develop software systems and algorithms that can integrate these datasets and begin to support a more democratic model where individuals can capture and track their own medical information through biometric devices and personal genome sequencing? Such systems will need to provide the necessary user interactions to work with the trillions of data points needed to make scientific discoveries. Here, we describe novel approaches in the genesis and processing of such data, models to integrate these data, and the increasing ubiquity of self-reporting and self-measured genomics and health data.

Limitations of the human reference genome for personalized genomics.

Data from the 1000 genomes project (1KGP) and Complete Genomics (CG) have dramatically increased the numbers of known genetic variants and challenge several assumptions about the reference genome and its uses in both clinical and research settings. Specifically, 34% of published array-based GWAS studies for a variety of diseases utilize probes that overlap unanticipated single nucleotide polymorphisms (SNPs), indels, or structural variants. Linkage disequilibrium (LD) block length depends on the numbers of markers used, and the mean LD block size decreases from 16 kb to 7 kb,when HapMap-based calculations are compared to blocks computed from1KGP data. Additionally, when 1KGP and CG variants are compared, 19% of the single nucleotide variants (SNVs) reported from common genomes are unique to one dataset; likely a result of differences in data collection methodology, alignment of reads to the reference genome, and variant-calling algorithms. Together these observations indicate that current research resources and informatics methods do not adequately account for the high level of variation that already exists in the human population and significant efforts are needed to create resources that can accurately assess personal genomics for health, disease, and predict treatment outcomes.

Transcriptional profiling by sequencing of oropharyngeal cancer.

OBJECTIVE: To compare full transcriptome expression levels of matched tumor and normal samples from patients with oropharyngeal carcinoma stratified by known tumor etiologic factors. PATIENTS AND METHODS: Full transcriptome sequencing was analyzed for 10 matched tumor and normal tissue samples from patients with previously untreated oropharyngeal carcinoma. Transcriptomes were analyzed using massively parallel messenger RNA sequencing and validated using the NanoString nCounter system. Global gene expression levels were compared in samples grouped by smoking status and human papillomavirus status. This study was completed between June 10, 2010, and June 30, 2011. RESULTS: Global gene expression analysis indicated tumor tissue from former smokers grouped more closely to the never smokers than the current smokers. Pathway analysis revealed alterations in the expression of genes involved in the p53 DNA damage-repair pathway, including CHEK2 and ATR, which display patterns of increased expression that is associated with human papillomavirus-negative current smokers rather than former or never smokers. CONCLUSION: These findings support the application of messenger RNA sequencing technology as an important clinical tool for more accurately stratifying patients based on individual tumor biology with the goal of improving our understanding of tumor prognosis and treatment response, ultimately leading to individualized patient care strategies.

Standardizing the next generation of bioinformatics software development with BioHDF (HDF5).

Next Generation Sequencing technologies are limited by the lack of standard bioinformatics infrastructures that can reduce data storage, increase data processing performance, and integrate diverse information. HDF technologies address these requirements and have a long history of use in data-intensive science communities. They include general data file formats, libraries, and tools for working with the data. Compared to emerging standards, such as the SAM/BAM formats, HDF5-based systems demonstrate significantly better scalability, can support multiple indexes, store multiple data types, and are self-describing. For these reasons, HDF5 and its BioHDF extension are well suited for implementing data models to support the next generation of bioinformatics applications.

Genome sequence of a serotype M3 strain of group A Streptococcus: phage-encoded toxins, the high-virulence phenotype, and clone emergence.

Genome sequences are available for many bacterial strains, but there has been little progress in using these data to understand the molecular basis of pathogen emergence and differences in strain virulence. Serotype M3 strains of group A Streptococcus (GAS) are a common cause of severe invasive infections with unusually high rates of morbidity and mortality. To gain insight into the molecular basis of this high-virulence phenotype, we sequenced the genome of strain MGAS315, an organism isolated from a patient with streptococcal toxic shock syndrome. The genome is composed of 1,900,521 bp, and it shares approximately 1.7 Mb of related genetic material with genomes of serotype M1 and M18 strains. Phage-like elements account for the great majority of variation in gene content relative to the sequenced M1 and M18 strains. Recombination produces chimeric phages and strains with previously uncharacterized arrays of virulence factor genes. Strain MGAS315 has phage genes that encode proteins likely to contribute to pathogenesis, such as streptococcal pyrogenic exotoxin A (SpeA) and SpeK, streptococcal superantigen (SSA), and a previously uncharacterized phospholipase A(2) (designated Sla). Infected humans had anti-SpeK, -SSA, and -Sla antibodies, indicating that these GAS proteins are made in vivo. SpeK and SSA were pyrogenic and toxic for rabbits. Serotype M3 strains with the phage-encoded speK and sla genes increased dramatically in frequency late in the 20th century, commensurate with the rise in invasive disease caused by M3 organisms. Taken together, the results show that phage-mediated recombination has played a critical role in the emergence of a new, unusually virulent clone of serotype M3 GAS.

Genome sequence and comparative microarray analysis of serotype M18 group A Streptococcus strains associated with acute rheumatic fever outbreaks.

Acute rheumatic fever (ARF), a sequelae of group A Streptococcus (GAS) infection, is the most common cause of preventable childhood heart disease worldwide. The molecular basis of ARF and the subsequent rheumatic heart disease are poorly understood. Serotype M18 GAS strains have been associated for decades with ARF outbreaks in the U.S. As a first step toward gaining new insight into ARF pathogenesis, we sequenced the genome of strain MGAS8232, a serotype M18 organism isolated from a patient with ARF. The genome is a circular chromosome of 1,895,017 bp, and it shares 1.7 Mb of closely related genetic material with strain SF370 (a sequenced serotype M1 strain). Strain MGAS8232 has 178 ORFs absent in SF370. Phages, phage-like elements, and insertion sequences are the major sources of variation between the genomes. The genomes of strain MGAS8232 and SF370 encode many of the same proven or putative virulence factors. Importantly, strain MGAS8232 has genes encoding many additional secreted proteins involved in human-GAS interactions, including streptococcal pyrogenic exotoxin A (scarlet fever toxin) and two uncharacterized pyrogenic exotoxin homologues, all phage-associated. DNA microarray analysis of 36 serotype M18 strains from diverse localities showed that most regions of variation were phages or phage-like elements. Two epidemics of ARF occurring 12 years apart in Salt Lake City, UT, were caused by serotype M18 strains that were genetically identical, or nearly so. Our analysis provides a critical foundation for accelerated research into ARF pathogenesis and a molecular framework to study the plasticity of GAS genomes.