Aleksandra Gruca

Motivation: Short tandem repeats (STRs) are abundant in protein sequences and play an important role in determining their structures and functions. Strikingly, the unusual compositional characteristics of tandem repeats break classical sequence analysis tools. Results: We here establish the first algorithm to effectively identify and cluster STRs: Graph-Based Sequence Clustering (GBSC) features linear time complexity, and clusters protein sequence fragments based on their STRs, while allowing for insertions and mutations, supporting an analysis of imperfect or cryptic repeats. Due to its computational efficacy, our algorithm can be used to systematically scan for patterns in large datasets. We compare our method both with state-of-the-art methods for identifying STRs in proteins and alternative clustering approaches. Unlike existing STR analysis methods, GBSC clusters repeat patterns rather than raw sequences, operating at the level of structural repeat identity, while tolerating biological variations and preventing erroneous merging of structurally and functionally distinct motifs. Whereas functional annotation is typically only available at the protein level, the functions of individual STRs and sequences of adjacent STRs remains largely unknown. On a challenging use-case we here demonstrate and discuss how our method can be used to associate previously unannotated repetitive protein fragments with similar ones, allowing a transfer of annotation by similarity. For the first time, GBSC offers a tool that systematically extends this fundamental bioinformatics principle to low-complexity regions across large datasets. Availability and Implementation: GBSC is available at GitHub https://github.com/patryk-jarnot/GBSC and https://doi.org/10.5281/zenodo.18965247. The data and scripts to reproduce the analysis are available at https://doi.org/10.5281/zenodo.16906653.

Despite striking successes in identifying novel biomarkers for improved patient stratification and predicting disease progression, numerous challenges remain in the effective integration and exploitation of multiomic data in biomedical applications beyond cancer, for which most bioinformatics strategies are developed and validated. That focus on cancer severely limits the effective development and advancement of algorithms in machine learning and artificial intelligence that do not suffer degraded out-of-domain performance. Generalizability and interpretability of models, however, are also required for robust insights that may translate into clinical practice. Work across different independent datasets is critical for establishing models robust towards unwanted variation in assays, protocols, and cohort populations. Disease-specific context like ethnicity, socioeconomic background, sex, lifestyle, disease phase, and tissue type also strongly affect molecular profiles. We here discuss atherosclerotic cardiovascular disease (ASCVD) as a high-impact non-cancer use case for the challenges remaining in the development and application of the latest bioinformatics approaches to multiomics data integration. ASCVD remains the leading cause of death globally. Disease aetiology, progression, and therapy outcome depend on a complex interplay of genetic, environmental, and lifestyle factors. Integrating these diverse data types effectively remains a challenge but holds transformative potential for personalized medicine. Discovery and access to data of sufficient diversity and extent form key bottlenecks. We here compile a first comprehensive overview of key data sets in ASCVD to complement the established cancer-focused resources as a foundation for future effective development and application of state-of-the-art bioinformatics tools for multiomic data integration.

Low Complexity Regions (LCRs) are segments of proteins with a low diversity of amino acid composition. These regions play important roles in proteins. However, annotations describing these functions are dispersed across databases and scientific literature. LCRAnnotationsDB aims to consolidate knowledge about LCRs and store relevant annotations in a single place. To unify redundant annotations, we assigned them categories based on similarity in function, protein structure, and biological process. Categories are organized hierarchically by linking them to Gene Ontology terms. The LCRAnnotationsDB database can be accessed at https://lcrannotdb.lcr-lab.org/.

The Polish Bioinformatic Society (PTBI) Symposium convenes annually at leading Polish Universities, and in 2023, the Silesian University of Technology hosted participants from all over the world. The 15th PTBI Symposium, spanning a 3-day duration and divided into four scientific sessions, gathered around 100 participants and centered on research related to machine learning in biomedicine, RNA structure algorithms, next-generation sequencing methods, and microbiome analysis but was not limited to only those topics. The meeting also recognized outstanding research conducted by young scientists by awarding the best poster and best talk. Finally, the awards for the best PhD, MSc, and BSc thesis in bioinformatics defended in Poland were given. This report summarizes the key highlights and outcomes of the meeting.

Low complexity regions are fragments of protein sequences composed of only a few types of amino acids. These regions frequently occur in proteins and can play an important role in their functions. However, scientists are mainly focused on regions characterized by high diversity of amino acid composition. Similarity between regions of protein sequences frequently reflect functional similarity between them. In this article, we discuss strengths and weaknesses of the similarity analysis of low complexity regions using BLAST, HHblits and CD-HIT. These methods are considered to be the gold standard in protein similarity analysis and were designed for comparison of high complexity regions. However, we lack specialized methods that could be used to compare the similarity of low complexity regions. Therefore, we investigated the existing methods in order to understand how they can be applied to compare such regions. Our results are supported by exploratory study, discussion of amino acid composition and biological roles of selected examples. We show that existing methods need improvements to efficiently search for similar low complexity regions. We suggest features that have to be re-designed specifically for comparing low complexity regions: scoring matrix, multiple sequence alignment, e-value, local alignment and clustering based on a set of representative sequences. Results of this analysis can either be used to improve existing methods or to create new methods for the similarity analysis of low complexity regions.

The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that neural networks can successfully predict future traffic conditions 1 hour into the future on simply aggregated GPS probe data in time and space bins. We thus reinterpreted the challenge of forecasting traffic conditions as a movie completion task. U-Nets proved to be the winning architecture, demonstrating an ability to extract relevant features in this complex real-world geo-spatial process. Building on the previous competitions, Traffic4cast 2021 now focuses on the question of model robustness and generalizability across time and space. Moving from one city to an entirely different city, or moving from pre-COVID times to times after COVID hit the world thus introduces a clear domain shift. We thus, for the first time, release data featuring such domain shifts. The competition now covers ten cities over 2 years, providing data compiled from over 10^12 GPS probe data. Winning solutions captured traffic dynamics sufficiently well to even cope with these complex domain shifts. Surprisingly, this seemed to require only the previous 1h traffic dynamic history and static road graph as input.

Patient multi-omics datasets are often characterized by a high dimensionality; however, usually only a small fraction of the features is informative, that is change in their value is directly related to the disease outcome or patient survival. In medical sciences, in addition to a robust feature selection procedure, the ability to discover human-readable patterns in the analyzed data is also desirable. To address this need, we created MAINE—Multi-omics Analysis and Exploration. The unique functionality of MAINE is the ability to discover multidimensional dependencies between the selected multi-omics features and event outcome prediction as well as patient survival probability. Learned patterns are visualized in the form of interpretable decision/survival trees and rules. MAINE is freely available at maine.ibemag.pl as an online web application. Supplementary data are available at Bioinformatics online.

New diseases constantly endanger the lives of populations, and, nowadays, they can spread easily and constitute a global threat. The COVID-19 pandemic has shown that the fight against a new disease may be difficult, especially at the initial stage of the epidemic, when medical knowledge is not complete and the symptoms are ambiguous. The use of machine learning tools can help to filter out those sick patients who do not need to be tested for spreading the pathogen, especially in the event of an overwhelming increase in disease transmission. This work presents a screening support system that can precisely identify patients who do not carry the disease. The decision of the system is made on the basis of patient survey data that are easy to collect. A case study on a data set of symptomatic COVID-19 patients shows that the system can be effective in the initial phase of the epidemic. The case study presents an analysis of two classifiers that were tuned to achieve an assumed acceptable threshold of negative predictive values during classification. Additionally, an explanation of the obtained classification models is presented. The explanation enables the users to understand the basis of the decision made by the model. The obtained classification models provide the basis for the DECODE service, which could serve as support in screening patients with COVID-19 disease at the initial stage of the pandemic. Moreover, the data set constituting the basis for the analyses performed is made available to the research community. This data set, consisting of more than 3000 examples, is based on questionnaires collected at a hospital in Poland.

In the DECODE project, data were collected from 3,114 surveys filled by symptomatic patients RT-qPCR tested for SARS-CoV-2 in a single university centre in March-September 2020. The population demonstrated balanced sex and age with 759 SARS-CoV-2( +) patients. The most discriminative symptoms in SARS-CoV-2( +) patients at early infection stage were loss of taste/smell (OR = 3.33,p < 0.0001), body temperature above 38℃ (OR = 1.67,p < 0.0001), muscle aches (OR = 1.30,p = 0.0242), headache (OR = 1.27,p = 0.0405), cough (OR = 1.26,p = 0.0477). Dyspnea was more often reported among SARS-CoV-2(-) (OR = 0.55,p < 0.0001). Cough and dyspnea were 3.5 times more frequent among SARS-CoV-2(-) (OR = 0.28,p < 0.0001). Co-occurrence of cough, muscle aches, headache, loss of taste/smell (OR = 4.72,p = 0.0015) appeared significant, although co-occurrence of two symptoms only, cough and loss of smell or taste, means OR = 2.49 (p < 0.0001). Temperature > 38℃ with cough was most frequent in men (20%), while loss of taste/smell with cough in women (17%). For younger people, taste/smell impairment is sufficient to characterise infection, whereas in older patients co-occurrence of fever and cough is necessary. The presented study objectifies the single symptoms and interactions significance in COVID-19 diagnoses and demonstrates diverse symptomatology in patient groups.

Today, academic researchers benefit from the changes driven by digital technologies and the enormous growth of knowledge and data, on globalisation, enlargement of the scientific community, and the linkage between different scientific communities and the society. To fully benefit from this development, however, information needs to be shared openly and transparently. Digitalisation plays a major role here because it permeates all areas of business, science and society and is one of the key drivers for innovation and international cooperation. To address the resulting opportunities, the EU promotes the development and use of collaborative ways to produce and share knowledge and data as early as possible in the research process, but also to appropriately secure results with the European strategy for Open Science (OS). It is now widely recognised that making research results more accessible to all societal actors contributes to more effective and efficient science; it also serves as a boost for innovation in the public and private sectors. However for research data to be findable, accessible, interoperable and reusable the use of standards is essential. At the metadata level, considerable efforts in standardisation have already been made (e.g. Data Management Plan and FAIR Principle etc.), whereas in context with the raw data these fundamental efforts are still fragmented and in some cases completely missing. The CHARME consortium, funded by the European Cooperation in Science and Technology (COST) Agency, has identified needs and gaps in the field of standardisation in the life sciences and also discussed potential hurdles for implementation of standards in current practice. Here, the authors suggest four measures in response to current challenges to ensure a high quality of life science research data and their re-usability for research and innovation.