adLIMS: a customized open source software that allows bridging clinical and basic molecular research studies

ISAnalytics enables longitudinal and high-throughput clonal tracking studies in hematopoietic stem cell gene therapy applications

Longitudinal clonal tracking studies based on high-throughput sequencing technologies supported safety and long-term efficacy and unraveled hematopoietic reconstitution in many gene therapy applications with unprecedented resolution. However, monitoring patients over a decade-long follow-up entails a constant increase of large data volume with the emergence of critical computational challenges, unfortunately not addressed by currently available tools. Here we present ISAnalytics, a new R package for comprehensive and high-throughput clonal tracking studies using vector integration sites as markers of cellular identity. Once identified the clones externally from ISAnalytics and imported in the package, a wide range of implemented functionalities are available to users for assessing the safety and long-term efficacy of the treatment, here described in a clinical trial use case for Hurler disease, and for supporting hematopoietic stem cell biology in vivo with longitudinal analysis of clones over time, proliferation and differentiation. ISAnalytics is conceived to be metadata-driven, enabling users to focus on biological questions and hypotheses rather than on computational aspects. ISAnalytics can be fully integrated within laboratory workflows and standard procedures. Moreover, ISAnalytics is designed with efficient and scalable data structures, benchmarked with previous methods, and grants reproducibility and full analytical control through interactive web-reports and a module with Shiny interface. The implemented functionalities are flexible for all viral vector-based clonal tracking applications as well as genetic barcoding or cancer immunotherapies.

Construction of Hospital Human Resource Information Management System under the Background of Artificial Intelligence

Under the background of artificial intelligence (AI), a human resource information management system was designed to facilitate hospital human resource management and improve hospital management efficiency. Based on AI, SOA was constructed and Java2 platform enterprise edition (J2EE) was combined with Java to design and research hospital human resource information management system. In addition, the function and performance required by the system were tested. The results showed that the designed system showed high safety in requirement analysis and performance. The function focused mainly on the systematic analysis of personnel management, recruitment management, organization and personnel management, and patient medical information. The constructed system could work normally and achieve the efficiency of hospital human resource management. The evaluation response time of system home page access was less than 1 second when 300 users were concurrent, and the utilization rate of service CPU was lower than 50% without abnormal memory fluctuation. The concurrent response time of all 20 managers online was less than 5 seconds, and the utilization rate of the service was lower than 70%. When the information of 100 employees in the system was queried concurrently, the average CPU utilization of the database server exceeded 90%. After performance optimization, the test result showed that the transaction response time was reduced to 0.23 seconds, which met the target requirement. In conclusion, the proposed intelligent human resource management system could reduce hospital management cost and the high sharing of human resource information provided a reference for the decision-making system of hospital leaders.

SiCoDEA: A Simple, Fast and Complete App for Analyzing the Effect of Individual Drugs and Their Combinations

The administration of combinations of drugs is a method widely used in the treatment of different pathologies as it can lead to an increase in the therapeutic effect and a reduction in the dose compared to the administration of single drugs. For these reasons, it is of interest to study combinations of drugs and to determine whether a specific combination has a synergistic, antagonistic or additive effect. Various mathematical models have been developed, which use different methods to evaluate the synergy of a combination of drugs. We have developed an open access and easy to use app that allows different models to be explored and the most fitting to be chosen for the specific experimental data: SiCoDEA (Single and Combined Drug Effect Analysis). Despite the existence of other tools for drug combination analysis, SiCoDEA remains the most complete and flexible since it offers options such as outlier removal or the ability to choose between different models for analysis. SiCoDEA is an easy to use tool for analyzing drug combination data and to have a view of the various steps and offer different results based on the model chosen.

Digital Management Systems in Academic Health Sciences Laboratories: A Scoping Review

Good laboratory practices (GLP) increase the quality and traceability of results in health sciences research. However, factors such as high staff turnover, insufficient resources, and a lack of training for managers may limit their implementation in research and academic laboratories. This Scoping Review aimed to identify digital tools for managing academic health sciences and experimental medicine laboratories and their relationship with good practices. Following the PRISMA-ScR 2018 criteria, a search strategy was conducted until April 2021 in the databases PUBMED, Web of Sciences, and Health Virtual Library. A critical appraisal of the selected references was conducted, followed by data charting. The search identified twenty-one eligible articles, mainly originated from high-income countries, describing the development and/or implementation of thirty-two electronic management systems. Most studies described software functionalities, while nine evaluated and discussed impacts on management, reporting both improvements in the workflow and system limitations during implementation. In general, the studies point to a contribution to different management issues related to GLP principles. In conclusion, this review identified evolving evidence that digital laboratory management systems may represent important tools in compliance with the principles of good practices in experimental medicine and health sciences research.

Integration of "omics" Data and Phenotypic Data Within a Unified Extensible Multimodal Framework

Analysis of “omics” data is often a long and segmented process, encompassing multiple stages from initial data collection to processing, quality control and visualization. The cross-modal nature of recent genomic analyses renders this process challenging to both automate and standardize; consequently, users often resort to manual interventions that compromise data reliability and reproducibility. This in turn can produce multiple versions of datasets across storage systems. As a result, scientists can lose significant time and resources trying to execute and monitor their analytical workflows and encounter difficulties sharing versioned data. In 2015, the Ludmer Centre for Neuroinformatics and Mental Health at McGill University brought together expertise from the Douglas Mental Health University Institute, the Lady Davis Institute and the Montreal Neurological Institute (MNI) to form a genetics/epigenetics working group. The objectives of this working group are to: (i) design an automated and seamless process for (epi)genetic data that consolidates heterogeneous datasets into the LORIS open-source data platform; (ii) streamline data analysis; (iii) integrate results with provenance information; and (iv) facilitate structured and versioned sharing of pipelines for optimized reproducibility using high-performance computing (HPC) environments via the CBRAIN processing portal. This article outlines the resulting generalizable “omics” framework and its benefits, specifically, the ability to: (i) integrate multiple types of biological and multi-modal datasets (imaging, clinical, demographics and behavioral); (ii) automate the process of launching analysis pipelines on HPC platforms; (iii) remove the bioinformatic barriers that are inherent to this process; (iv) ensure standardization and transparent sharing of processing pipelines to improve computational consistency; (v) store results in a queryable web interface; (vi) offer visualization tools to better view the data; and (vii) provide the mechanisms to ensure usability and reproducibility. This framework for workflows facilitates brain research discovery by reducing human error through automation of analysis pipelines and seamless linking of multimodal data, allowing investigators to focus on research instead of data handling.

The High-Throughput Analyses Era: Are We Ready for the Data Struggle?

Recent and rapid technological advances in molecular sciences have dramatically increased the ability to carry out high-throughput studies characterized by big data production. This, in turn, led to the consequent negative effect of highlighting the presence of a gap between data yield and their analysis. Indeed, big data management is becoming an increasingly important aspect of many fields of molecular research including the study of human diseases. Now, the challenge is to identify, within the huge amount of data obtained, that which is of clinical relevance. In this context, issues related to data interpretation, sharing and storage need to be assessed and standardized. Once this is achieved, the integration of data from different -omic approaches will improve the diagnosis, monitoring and therapy of diseases by allowing the identification of novel, potentially actionably biomarkers in view of personalized medicine.

VISPA2: a scalable pipeline for high-throughput identification and annotation of vector integration sites

Background

Bioinformatics tools designed to identify lentiviral or retroviral vector insertion sites in the genome of host cells are used to address the safety and long-term efficacy of hematopoietic stem cell gene therapy applications and to study the clonal dynamics of hematopoietic reconstitution. The increasing number of gene therapy clinical trials combined with the increasing amount of Next Generation Sequencing data, aimed at identifying integration sites, require both highly accurate and efficient computational software able to correctly process “big data” in a reasonable computational time.

Results

Here we present VISPA2 (Vector Integration Site Parallel Analysis, version 2), the latest optimized computational pipeline for integration site identification and analysis with the following features: (1) the sequence analysis for the integration site processing is fully compliant with paired-end reads and includes a sequence quality filter before and after the alignment on the target genome; (2) an heuristic algorithm to reduce false positive integration sites at nucleotide level to reduce the impact of Polymerase Chain Reaction or trimming/alignment artifacts; (3) a classification and annotation module for integration sites; (4) a user friendly web interface as researcher front-end to perform integration site analyses without computational skills; (5) the time speedup of all steps through parallelization (Hadoop free).

Conclusions

We tested VISPA2 performances using simulated and real datasets of lentiviral vector integration sites, previously obtained from patients enrolled in a hematopoietic stem cell gene therapy clinical trial and compared the results with other preexisting tools for integration site analysis. On the computational side, VISPA2 showed a > 6-fold speedup and improved precision and recall metrics (1 and 0.97 respectively) compared to previously developed computational pipelines. These performances indicate that VISPA2 is a fast, reliable and user-friendly tool for integration site analysis, which allows gene therapy integration data to be handled in a cost and time effective fashion. Moreover, the web access of VISPA2 (http://openserver.itb.cnr.it/vispa/) ensures accessibility and ease of usage to researches of a complex analytical tool. We released the source code of VISPA2 in a public repository (https://bitbucket.org/andreacalabria/vispa2).

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1937-9) contains supplementary material, which is available to authorized users.

LIMS and Clinical Data Management

In order to achieve more accurate disease prevention, diagnosis, and treatment, clinical and genetic data need extensive and systematically associated study. As one way to achieve precision medicine, a laboratory information management system (LIMS) can effectively associate clinical data in a macrocosmic aspect and genomic data in a microcosmic aspect. This chapter summarizes the application of the LIMS in a clinical data management and implementation mode. It also discusses the principles of a LIMS in clinical data management, as well as the opportunities and challenges in the context of medical informatics.

Preface: BITS2014, the annual meeting of the Italian Society of Bioinformatics

This Preface introduces the content of the BioMed Central journal Supplements related to BITS2014 meeting, held in Rome, Italy, from the 26th to the 28th of February, 2014.