Data challenges of biomedical researchers in the age of omics

microRNAs as molecular tools for brain health: Neuroprotective potential in neurodegenerative disorders

As research on microRNAs (miRNAs) advances, it is becoming increasingly clear that these small molecules play crucial roles in the central nervous system (CNS). They are involved in various essential neuronal functions, with specific miRNAs preferentially expressed in different cell types within the nervous system. Notably, certain miRNAs are found at higher levels in the brain and spinal cord compared to other tissues, suggesting they may have specialized functions in the CNS. miRNAs associated with long-term neurodegenerative changes could serve as valuable tools for early treatment decisions and disease monitoring. The significance of miRNAs such as miR-320, miR-146 and miR-29 in the early diagnosis of neurodegenerative disorders becomes evident, especially considering that many neurological and physical symptoms manifest only after substantial degeneration of specific neurons. Interestingly, serum miRNA levels such as miR-92 and miR-486 may correlate with various MRI parameters in multiple sclerosis. Targeting miRNAs using antisense strategies, such as antisense miR-146 and miR-485, may provide advantages over targeting mRNAs, as a single anti-miRNA can regulate multiple disease-related genes. In the future, anti-miRNA-based therapeutic approaches could be integrated into the clinical management of neurological diseases. Certain miRNAs, including miR-223, miR-106, miR-181, and miR-146, contribute to the pathogenesis of various neurodegenerative diseases and thus warrant greater attention. This knowledge could pave the way for the identification of new diagnostic, prognostic, and theranostic biomarkers, and potentially guiding the development of RNA-based therapeutic strategies. This review highlights recent research on the roles of miRNAs in the nervous system, particularly their protective functions in neurodegenerative disorders.

Leveraging large language models for data analysis automation

Data analysis is constrained by a shortage of skilled experts, particularly in biology, where detailed data analysis and subsequent interpretation is vital for understanding complex biological processes and developing new treatments and diagnostics. One possible solution to this shortage in experts would be making use of Large Language Models (LLMs) for generating data analysis pipelines. However, although LLMs have shown great potential when used for code generation tasks, questions regarding the accuracy of LLMs when prompted with domain expert questions such as omics related data analysis questions, remain unanswered. To address this, we developed mergen, an R package that leverages LLMs for data analysis code generation and execution. We evaluated the performance of this data analysis system using various data analysis tasks for genomics. Our primary goal is to enable researchers to conduct data analysis by simply describing their objectives and the desired analyses for specific datasets through clear text. Our approach improves code generation via specialized prompt engineering and error feedback mechanisms. In addition, our system can execute the data analysis workflows prescribed by the LLM providing the results of the data analysis workflow for human review. Our evaluation of this system reveals that while LLMs effectively generate code for some data analysis tasks, challenges remain in executable code generation, especially for complex data analysis tasks. The best performance was seen with the self-correction mechanism, in which self-correct was able to increase the percentage of executable code when compared to the simple strategy by 22.5% for tasks of complexity 2. For tasks for complexity 3, 4 and 5, this increase was 52.5%, 27.5% and 15% respectively. Using a chi-squared test, it was shown that significant differences could be found using the different prompting strategies. Our study contributes to a better understanding of LLM capabilities and limitations, providing software infrastructure and practical insights for their effective integration into data analysis workflows.

Partnering with health sciences libraries to address challenges in bioimaging data management and sharing

Federal mandates, publishing requirements, and an interest in open science have all generated renewed attention on research data management and, in particular, data sharing practices. Due to the size and types of data they produce, bioimaging researchers confront specific challenges in aligning their data with FAIR principles, ensuring that it is findable, accessible, interoperable, and reusable. Although not always recognized by researchers, libraries can, and have been, offering support for data throughout its lifecycle by assisting with data management planning, acquisition, processing and analysis, and sharing and reuse of data. Libraries can educate researchers on best practices for research data management and sharing, facilitate connections to experts by coordinating sessions using peer educators and appropriate vendors, help assess the needs of different researcher groups to identify challenges or gaps, recommend appropriate repositories to make data as accessible as possible, and comply with funder and publisher requirements. As a centralized service within an institution, health sciences libraries have the capability to bridge silos and connect bioimaging researchers with specialized data support across campus and beyond.

Proteomics data analysis using multiple statistical approaches identified proteins and metabolic networks associated with sucrose accumulation in sugarcane

Background

Sugarcane is the most important sugar crop, contributing > 80% of global sugar production. High sucrose content is a key target of sugarcane breeding, yet sucrose improvement in sugarcane remains extremely slow for decades. Molecular breeding has the potential to break through the genetic bottleneck of sucrose improvement. Dissecting the molecular mechanism(s) and identifying the key genetic elements controlling sucrose accumulation will accelerate sucrose improvement by molecular breeding. In our previous work, a proteomics dataset based on 12 independent samples from high- and low-sugar genotypes treated with ethephon or water was established. However, in that study, employing conventional analysis, only 25 proteins involved in sugar metabolism were identified .

Results

In this work, the proteomics dataset used in our previous study was reanalyzed by three different statistical approaches, which include a logistic marginal regression, a penalized multiple logistic regression named Elastic net, as well as a Bayesian multiple logistic regression method named Stochastic search variable selection (SSVS) to identify more sugar metabolism-associated proteins. A total of 507 differentially abundant proteins (DAPs) were identified from this dataset, with 5 of them were validated by western blot. Among the DAPs, 49 proteins were found to participate in sugar metabolism-related processes including photosynthesis, carbon fixation as well as carbon, amino sugar, nucleotide sugar, starch and sucrose metabolism. Based on our studies, a putative network of key proteins regulating sucrose accumulation in sugarcane is proposed, with glucose-6-phosphate isomerase, 2-phospho-D-glycerate hydrolyase, malate dehydrogenase and phospho-glycerate kinase, as hub proteins.

Conclusions

The sugar metabolism-related proteins identified in this work are potential candidates for sucrose improvement by molecular breeding. Further, this work provides an alternative solution for omics data processing.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08768-2.

Peer Teaching as Bioinformatics Training Strategy: Incentives, Challenges, and Benefits

Bioinformatics is essential for basic and clinical research. Peer-to-peer (P2P) teaching was used to respond to the bioinformatics training needs at a research-intensive institution. In addition to the data collected from the workshops, personal experiences of the teachers were used to understand incentives, challenges, and benefits of P2P teaching. Developing communication skills such as confidence in teaching, explaining complex concepts, and better understanding of topics benefited P2P teachers. Lack of time and classroom management were identified as major challenges. Hence, P2P teaching can be beneficial not only for bioinformatics trainees but also as a professional development opportunity for peer teachers.

Interdisciplinary approach to biomedical research: a panacea to efficient research output during the global pandemic

Biomedical research is rapidly growing due to inventions and developments in science and technology. Several interdisciplinary fields should be combined to find the remedy of diseases including pandemics. To accomplish this, interdisciplinary research is a prerequisite. Using improved techniques in microscopy and genetic engineering, the systemic perspective of the human body and related diseases can be found. Recent genetic-based inheritance studies of diseases, understanding various omics, stem cell systems, and gene editing tools including CRISPR relevant to biomedical research require multidisciplinary approach. Improvements in the field of bioinformatics and efficient use of model organisms in clinical testing including drug assessment are important disciplines common in different researches. The merging of different closely related areas of medical research will produce suitable changes in diagnosis and treatment. In the present scenario of increased global pandemic hits like COVID-19, an understanding on the interdisciplinary approach is needed for controlling the spread and finding vaccines.

PUMAA: A Platform for Accessible Microbiome Analysis in the Undergraduate Classroom

Improvements in high-throughput sequencing makes targeted amplicon analysis an ideal method for the study of human and environmental microbiomes by undergraduates. Multiple bioinformatics programs are available to process and interpret raw microbial diversity datasets, and the choice of programs to use in curricula is largely determined by student learning goals. Many of the most commonly used microbiome bioinformatics platforms offer end-to-end data processing and data analysis using a command line interface (CLI), but the downside for novice microbiome researchers is the steep learning curve often required. Alternatively, some sequencing providers include processing of raw data and taxonomy assignments as part of their pipelines. This, when coupled with available web-based or graphical user interface (GUI) analysis and visualization tools, eliminates the need for students or instructors to have extensive CLI experience. However, lack of universal data formats can make integration of these tools challenging. For example, tools for upstream and downstream analyses frequently use multiple different data formats which then require writing custom scripts or hours of manual work to make the files compatible. Here, we describe a microbial ecology bioinformatics curriculum that focuses on data analysis, visualization, and statistical reasoning by taking advantage of existing web-based and GUI tools. We created the Program for Unifying Microbiome Analysis Applications (PUMAA), which solves the problem of inconsistent files by formatting the output files from several raw data processing programs to seamlessly transition to a suite of GUI programs for analysis and visualization of microbiome taxonomic and inferred functional profiles. Additionally, we created a series of tutorials to accompany each of the microbiome analysis curricular modules. From pre- and post-course surveys, students in this curriculum self-reported conceptual and confidence gains in bioinformatics and data analysis skills. Students also demonstrated gains in biologically relevant statistical reasoning based on rubric-guided evaluations of open-ended survey questions and the Statistical Reasoning in Biology Concept Inventory. The PUMAA program and associated analysis tutorials enable students and researchers with no computational experience to effectively analyze real microbiome datasets to investigate real-world research questions.

Molecular Biology Information Service: an innovative medical library-based bioinformatics support service for biomedical researchers

Biomedical researchers are increasingly reliant on obtaining bioinformatics training in order to conduct their research. Here we present a model that academic institutions may follow to provide such training for their researchers, based on the Molecular Biology Information Service (MBIS) of the Health Sciences Library System, University of Pittsburgh (Pitt). The MBIS runs a four-facet service with the following goals: (1) identify, procure and implement commercially licensed bioinformatics software, (2) teach hands-on workshops using bioinformatics tools to solve research questions, (3) provide in-person and email consultations on software/databases and (4) maintain a web portal providing overall guidance on the access and use of bioinformatics resources and MBIS-created webtools. This paper describes these facets of MBIS activities from 2006 to 2018, including outcomes from a survey measuring attitudes of Pitt researchers about MBIS service and performance.

A Combined Visualization Method for Multivariate Data Analysis. Application to Knee Kinematic and Clinical Parameters Relationships

This paper aims to analyze the correlation structure between the kinematic and clinical parameters of an end-staged knee osteoarthritis population. The kinematic data are a set of characteristics derived from 3D knee kinematic patterns. The clinical parameters include the answers of a clinical questionnaire and the patient’s demographic characteristics. The proposed method performs, first, a regularized canonical correlation analysis (RCCA) to evaluate the multivariate relationship between the clinical and kinematic datasets, and second, a combined visualization method to better understand the relationships between these multivariate data. Results show the efficiency of using different and complementary visual representation tools to highlight hidden relationships and find insights in data.

NetR and AttR, Two New Bioinformatic Tools to Integrate Diverse Datasets into Cytoscape Network and Attribute Files

High-throughput technologies have allowed researchers to obtain genome-wide data from a wide array of experimental model systems. Unfortunately, however, new data generation tends to significantly outpace data re-utilization, and most high throughput datasets are only rarely used in subsequent studies or to generate new hypotheses to be tested experimentally. The reasons behind such data underutilization include a widespread lack of programming expertise among experimentalist biologists to carry out the necessary file reformatting that is often necessary to integrate published data from disparate sources. We have developed two programs (NetR and AttR), which allow experimental biologists with little to no programming background to integrate publicly available datasets into files that can be later visualized with Cytoscape to display hypothetical networks that result from combining individual datasets, as well as a series of published attributes related to the genes or proteins in the network. NetR also allows users to import protein and genetic interaction data from InterMine, which can further enrich a network model based on curated information. We expect that NetR/AttR will allow experimental biologists to mine a largely unexploited wealth of data in their fields and facilitate their integration into hypothetical models to be tested experimentally.