Baobab LIMS: An open source biobank laboratory information management system for resource-limited settings

Philosophiae Doctor - PhDA laboratory information management system (LIMS) is central to the informatics infrastructure that underlies biobanking activities. To date, a wide range of commercial and open source LIMS are available. The decision to opt for one LIMS over another is often influenced by the needs of the biobank clients and researchers, as well as available financial resources. However, to find a LIMS that incorporates all possible requirements of a biobank may often be a complicated endeavour. The need to implement biobank standard operation procedures as well as stimulate the use of standards for biobank data representation motivated the development of Baobab LIMS, an open source LIMS for Biobanking. Baobab LIMS comprises modules for biospecimen kit assembly, shipping of biospecimen kits, storage management, analysis requests, reporting, and invoicing. Baobab LIMS is based on the Plone web-content management framework, a server-client-based system, whereby the end user is able to access the system securely through the internet on a standard web browser, thereby eliminating the need for standalone installations on all machines. The Baobab LIMS components were tested and evaluated in three human biobanks. The testing of the LIMS modules aided in the mapping of the biobanks requirements to the LIMS functionalities, and furthermore, it helped to reveal new user suggestions, such as the enhancement of the online documentation. The user suggestions are demonstrated to be important for both LIMS strengthen and biobank sustainability. Ultimately, the practical LIMS evaluations showed the ability of Boabab LIMS to be used in the management of human biobanks operations of relatively different biobanking workflows

Assessment of the progression of kidney renal clear cell carcinoma using transcriptional profiles revealed new cancer subtypes with variable prognosis

Background: Kidney renal clear cell carcinoma is the most prevalent subtype of renal cell carcinoma encompassing a heterogeneous group of malignancies. Accurate subtype identification and an understanding of the variables influencing prognosis are critical for personalized treatment, but currently limited. To facilitate the sub-classification of KIRC patients and improve prognosis, this study implemented a normalization method to track cancer progression by detecting the accumulation of genetic changes that occur throughout the multi-stage of cancer development.Objective: To reveal KIRC patients with different progression based on gene expression profiles using a normalization method. The aim is to refine molecular subtyping of KIRC patients associated with survival outcomes.Methods: RNA-sequenced gene expression of eighty-two KIRC patients were downloaded from UCSC Xena database. Advanced-stage samples were normalized with early-stage to account for differences in the multi-stage cancer progression’s heterogeneity. Hierarchical clustering was performed to reveal clusters that progress differently. Two techniques were applied to screen for significant genes within the clusters. First, differentially expressed genes (DEGs) were discovered by Limma, thereafter, an optimal gene subset was selected using Recursive Feature Elimination (RFE). The gene subset was subjected to Random Forest Classifier to evaluate the cluster prediction performance. Genes strongly associated with survival were identified utilizing Cox regression analysis. The model’s accuracy was assessed with Kaplan-Meier (K-M). Finally, a Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed.Results: Three clusters were revealed and categorized based on patients’ overall survival into short, intermediate, and long. A total of 231 DEGs were discovered of which RFE selected 48 genes. Random Forest Classifier revealed a 100% cluster prediction performance of the genes. Five genes were identified with significant diagnostic capacity. The downregulation of genes SALL4 and KRT15 were associated with favorable prognosis, while the upregulation of genes OSBPL11, SPATA18, and TAL2 were associated with favorable prognosis.Conclusion: The normalization method based on tumour progression from early to late stages of cancer development revealed the heterogeneity of KIRC and identified three potential new subtypes with different prognoses. This could be of great importance for the development of new targeted therapies for each subtype

Evaluation of protein purification techniques and effects of storage duration on lc-ms/ms analysis of archived ffpe human crc tissues

To elucidate cancer pathogenesis and its mechanisms at the molecular level, the collecting and characterization of large individual patient tissue cohorts are required. Since most pathology institutes routinely preserve biopsy tissues by standardized methods of formalin fixation and paraffin embedment, these archived FFPE tissues are important collections of pathology material that include patient metadata, such as medical history and treatments. FFPE blocks can be stored under ambient conditions for decades, while retaining cellular morphology, due to modifications induced by formalin. However, the effect of long-term storage, at resource-limited institutions in developing countries, on extractable protein quantity/quality has not yet been investigated. In addition, the optimal sample preparation techniques required for accurate and reproducible results from label-free LC-MS/MS analysis across block ages remains unclear. This study investigated protein extraction efficiency of 1, 5, and 10-year old human colorectal carcinoma resection tissue and assessed three different gel-free protein purification methods for label-free LC-MS/MS analysis. A sample size of n 17 patients per experimental group (with experiment power 0.7 and α 0.05, resulting in 70% confidence level) was selected

Changes in subcutaneous adipose tissue microRNA expression in response to exercise training in African women with obesity

The mechanisms that underlie exercise-induced adaptations in adipose tissue have not been elucidated, yet, accumulating studies suggest an important role for microRNAs (miRNAs). This study aimed to investigate miRNA expression in gluteal subcutaneous adipose tissue (GSAT) in response to a 12-week exercise intervention in South African women with obesity, and to assess depot-specific differences in miRNA expression in GSAT and abdominal subcutaneous adipose tissue (ASAT). In addition, the association between exercise-induced changes in miRNA expression and metabolic risk was evaluated. Women underwent 12-weeks of supervised aerobic and resistance training (n = 19) or maintained their regular physical activity during this period (n = 12). Exercise-induced miRNAs were identified in GSAT using Illumina sequencing, followed by analysis of differentially expressed miRNAs in GSAT and ASAT using quantitative real-time PCR. Associations between the changes (pre- and postexercise training) in miRNA expression and metabolic parameters were evaluated using Spearman’s correlation tests. Exercise training significantly increased the expression of miR-155-5p (1.5-fold, p = 0.045), miR-329-3p (2.1-fold, p < 0.001) and miR-377-3p (1.7-fold, p = 0.013) in GSAT, but not in ASAT. In addition, a novel miRNA, MYN0617, was identified in GSAT, with low expression in ASAT. The exercise-induced differences in miRNA expression were correlated with each other and associated with changes in high-density lipoprotein concentrations. Exercise training induced adipose-depot specific miRNA expression within subcutaneous adipose tissue depots from South African women with obesity. The significance of the association between exercise-induced miRNAs and metabolic risk warrants further investigation.The South African Medical Research Council (SAMRC) and the National Research Foundation of South Africa (NRF), Competitive Programme for Rated Researchers.http://www.nature.com/scientificreportsam2023Obstetrics and Gynaecolog

Developing reproducible bioinformatics analysis workflows for heterogeneous computing environments to support African genomics

Background: The Pan-African bioinformatics network, H3ABioNet, comprises 27 research institutions in 17 African countries. H3ABioNet is part of the Human Health and Heredity in Africa program (H3Africa), an African-led research consortium funded by the US National Institutes of Health and the UK Wellcome Trust, aimed at using genomics to study and improve the health of Africans. A key role of H3ABioNet is to support H3Africa projects by building bioinformatics infrastructure such as portable and reproducible bioinformatics workflows for use on heterogeneous African computing environments. Processing and analysis of genomic data is an example of a big data application requiring complex interdependent data analysis workflows. Such bioinformatics workflows take the primary and secondary input data through several computationally-intensive processing steps using different software packages, where some of the outputs form inputs for other steps. Implementing scalable, reproducible, portable and easy-to-use workflows is particularly challenging. Results: H3ABioNet has built four workflows to support (1) the calling of variants from high-throughput sequencing data; (2) the analysis of microbial populations from 16S rDNA sequence data; (3) genotyping and genome-wide association studies; and (4) single nucleotide polymorphism imputation. A week-long hackathon was organized in August 2016 with participants from six African bioinformatics groups, and US and European collaborators. Two of the workflows are built using the Common Workflow Language framework (CWL) and two using Nextflow. All the workflows are containerized for improved portability and reproducibility using Docker, and are publicly available for use by members of the H3Africa consortium and the international research community. Conclusion: The H3ABioNet workflows have been implemented in view of offering ease of use for the end user and high levels of reproducibility and portability, all while following modern state of the art bioinformatics data processing protocols. The H3ABioNet workflows will service the H3Africa consortium projects and are currently in use. All four workflows are also publicly available for research scientists worldwide to use and adapt for their respective needs. The H3ABioNet workflows will help develop bioinformatics capacity and assist genomics research within Africa and serve to increase the scientific output of H3Africa and its Pan-African Bioinformatics Network

Table2_Assessment of the progression of kidney renal clear cell carcinoma using transcriptional profiles revealed new cancer subtypes with variable prognosis.DOCX

Background: Kidney renal clear cell carcinoma is the most prevalent subtype of renal cell carcinoma encompassing a heterogeneous group of malignancies. Accurate subtype identification and an understanding of the variables influencing prognosis are critical for personalized treatment, but currently limited. To facilitate the sub-classification of KIRC patients and improve prognosis, this study implemented a normalization method to track cancer progression by detecting the accumulation of genetic changes that occur throughout the multi-stage of cancer development.Objective: To reveal KIRC patients with different progression based on gene expression profiles using a normalization method. The aim is to refine molecular subtyping of KIRC patients associated with survival outcomes.Methods: RNA-sequenced gene expression of eighty-two KIRC patients were downloaded from UCSC Xena database. Advanced-stage samples were normalized with early-stage to account for differences in the multi-stage cancer progression’s heterogeneity. Hierarchical clustering was performed to reveal clusters that progress differently. Two techniques were applied to screen for significant genes within the clusters. First, differentially expressed genes (DEGs) were discovered by Limma, thereafter, an optimal gene subset was selected using Recursive Feature Elimination (RFE). The gene subset was subjected to Random Forest Classifier to evaluate the cluster prediction performance. Genes strongly associated with survival were identified utilizing Cox regression analysis. The model’s accuracy was assessed with Kaplan-Meier (K-M). Finally, a Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed.Results: Three clusters were revealed and categorized based on patients’ overall survival into short, intermediate, and long. A total of 231 DEGs were discovered of which RFE selected 48 genes. Random Forest Classifier revealed a 100% cluster prediction performance of the genes. Five genes were identified with significant diagnostic capacity. The downregulation of genes SALL4 and KRT15 were associated with favorable prognosis, while the upregulation of genes OSBPL11, SPATA18, and TAL2 were associated with favorable prognosis.Conclusion: The normalization method based on tumour progression from early to late stages of cancer development revealed the heterogeneity of KIRC and identified three potential new subtypes with different prognoses. This could be of great importance for the development of new targeted therapies for each subtype.</p

Table1_Assessment of the progression of kidney renal clear cell carcinoma using transcriptional profiles revealed new cancer subtypes with variable prognosis.DOCX

Background: Kidney renal clear cell carcinoma is the most prevalent subtype of renal cell carcinoma encompassing a heterogeneous group of malignancies. Accurate subtype identification and an understanding of the variables influencing prognosis are critical for personalized treatment, but currently limited. To facilitate the sub-classification of KIRC patients and improve prognosis, this study implemented a normalization method to track cancer progression by detecting the accumulation of genetic changes that occur throughout the multi-stage of cancer development.Objective: To reveal KIRC patients with different progression based on gene expression profiles using a normalization method. The aim is to refine molecular subtyping of KIRC patients associated with survival outcomes.Methods: RNA-sequenced gene expression of eighty-two KIRC patients were downloaded from UCSC Xena database. Advanced-stage samples were normalized with early-stage to account for differences in the multi-stage cancer progression’s heterogeneity. Hierarchical clustering was performed to reveal clusters that progress differently. Two techniques were applied to screen for significant genes within the clusters. First, differentially expressed genes (DEGs) were discovered by Limma, thereafter, an optimal gene subset was selected using Recursive Feature Elimination (RFE). The gene subset was subjected to Random Forest Classifier to evaluate the cluster prediction performance. Genes strongly associated with survival were identified utilizing Cox regression analysis. The model’s accuracy was assessed with Kaplan-Meier (K-M). Finally, a Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed.Results: Three clusters were revealed and categorized based on patients’ overall survival into short, intermediate, and long. A total of 231 DEGs were discovered of which RFE selected 48 genes. Random Forest Classifier revealed a 100% cluster prediction performance of the genes. Five genes were identified with significant diagnostic capacity. The downregulation of genes SALL4 and KRT15 were associated with favorable prognosis, while the upregulation of genes OSBPL11, SPATA18, and TAL2 were associated with favorable prognosis.Conclusion: The normalization method based on tumour progression from early to late stages of cancer development revealed the heterogeneity of KIRC and identified three potential new subtypes with different prognoses. This could be of great importance for the development of new targeted therapies for each subtype.</p

Assessing computational genomics skills: Our experience in the H3ABioNet African bioinformatics network

The H3ABioNet pan-African bioinformatics network, which is funded to support the Human Heredity and Health in Africa (H3Africa) program, has developed node-assessment exer�cises to gauge the ability of its participating research and service groups to analyze typical genome-wide datasets being generated by H3Africa research groups. We describe a frame�work for the assessment of computational genomics analysis skills, which includes standard operating procedures, training and test datasets, and a process for administering the exer�cise. We present the experiences of 3 research groups that have taken the exercise and the impact on their ability to manage complex projects. Finally, we discuss the reasons why many H3ABioNet nodes have declined so far to participate and potential strategies to encourage them to do so