INSaFLU: an automated open web-based bioinformatics suite “from-reads” for influenza whole-genome-sequencing-based surveillance

Abstract

BackgroundA new era of flu surveillance has already started based on the genetic characterization and exploration of influenza virus evolution at whole-genome scale. Although this has been prioritized by national and international health authorities, the demanded technological transition to whole-genome sequencing (WGS)-based flu surveillance has been particularly delayed by the lack of bioinformatics infrastructures and/or expertise to deal with primary next-generation sequencing (NGS) data.ResultsWe developed and implemented INSaFLU (“INSide the FLU”), which is the first influenza-oriented bioinformatics free web-based suite that deals with primary NGS data (reads) towards the automatic generation of the output data that are actually the core first-line “genetic requests” for effective and timely influenza laboratory surveillance (e.g., type and sub-type, gene and whole-genome consensus sequences, variants’ annotation, alignments and phylogenetic trees). By handling NGS data collected from any amplicon-based schema, the implemented pipeline enables any laboratory to perform multi-step software intensive analyses in a user-friendly manner without previous advanced training in bioinformatics. INSaFLU gives access to user-restricted sample databases and projects management, being a transparent and flexible tool specifically designed to automatically update project outputs as more samples are uploaded. Data integration is thus cumulative and scalable, fitting the need for a continuous epidemiological surveillance during the flu epidemics. Multiple outputs are provided in nomenclature-stable and standardized formats that can be explored in situ or through multiple compatible downstream applications for fine-tuned data analysis. This platform additionally flags samples as “putative mixed infections” if the population admixture enrolls influenza viruses with clearly distinct genetic backgrounds, and enriches the traditional “consensus-based” influenza genetic characterization with relevant data on influenza sub-population diversification through a depth analysis of intra-patient minor variants. This dual approach is expected to strengthen our ability not only to detect the emergence of antigenic and drug resistance variants but also to decode alternative pathways of influenza evolution and to unveil intricate routes of transmission.ConclusionsIn summary, INSaFLU supplies public health laboratories and influenza researchers with an open “one size fits all” framework, potentiating the operationalization of a harmonized multi-country WGS-based surveillance for influenza virus.INSaFLU can be accessed through https://insaflu.insa.pt.Electronic supplementary materialThe online version of this article (10.1186/s13073-018-0555-0) contains supplementary material, which is available to authorized users.

Every fifth published metagenome is not available to science

“…'Data can also be used in innovative ways that bring economic benefits to citizens and businesses by releasing untapped enterprise and entrepreneurship' [8]. The leveraging of influenza virus (RNA) sequences [9] for the development of novel, more efficacious influenza vaccines [8] stands as one of a handful of clear-cut examples of the potential societal and economic benefits from Open Data. The emergence of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) also clearly demonstrates the synergy that can come from ensuring Open Access to sequencing data-bringing rapid insights that can stem the spread of a potentially pandemic virus in real time [10,11].…”

Mitogenome diversity of Aedes (Stegomyia) albopictus: Detection of multiple introduction events in Portugal

“…Core bioinformatics analyses were conducted using INSaFLU (https://insaflu.insa.pt/), a webbased platform for amplicon-based NGS data analysis [44]. Briefly, the bioinformatics pipeline (detailed in Borges et al [44]) involved: i) raw NGS reads quality analysis and improvement Table. For the integration of mosquitos circulating in Portugal into the global Ae.…”

“…Core bioinformatics analyses were conducted using INSaFLU (https://insaflu.insa.pt/), a webbased platform for amplicon-based NGS data analysis [44]. Briefly, the bioinformatics pipeline (detailed in Borges et al [44]) involved: i) raw NGS reads quality analysis and improvement Table. For the integration of mosquitos circulating in Portugal into the global Ae. albopictus genetic diversity, nucleotide consensus sequences of both COI gene and mitogenome were aligned against multiple sequences available at GenBank (183 COI and 26 mitogenome sequences previously reported at worldwide level; S1 and S2 Tables respectively) using MAFFT v. 7.313 [46].…”

“…Core bioinformatics analyses were conducted using INSaFLU ( https://insaflu.insa.pt/ ), a web-based platform for amplicon-based NGS data analysis [ 44 ]. Briefly, the bioinformatics pipeline (detailed in Borges et al [ 44 ]) involved: i) raw NGS reads quality analysis and improvement using FastQC v. 0.11.5 ( https://www.bioinformatics.babraham.ac.uk/projects/fastqc ) and Trimmomatic v. 0.27 [ 45 ] ( http://www.usadellab.org/cms/index.php?page=trimmomatic ), respectively; ii) reference-based mapping, consensus generation and variant detection using the multisoftware tool Snippy v. 3.2-dev ( https://github.com/tseemann/snippy ) (the captured sequence of the mitogenome of Ae . albopictus strain Rimini isolate 1#Rim1 haplogroup A1a1a1 was used as reference; NCBI accession number KX383916; positions 283–14702); and, iii) alignment of consensus sequences using MAFFT v. 7.313 [ 46 ] ( https://mafft.cbrc.jp/alignment/software/ ).…”

Infection and immunity: insights and therapeutic strategies through genomic analysis of the host, pathogen, and host–pathogen interaction

“…Responses to Ebola virus and Zika virus outbreaks, among others, have demonstrated an essential role of genomics in strategies for surveillance and management of infectious diseases. Discussing avian influenza, Lam and Pybus [ 8 ] forecast that successful epidemic control “on a global scale will require increased genomic surveillance in poorly characterized regions, timely data sharing, and the development of new analytical methods to test hypotheses concerning influenza virus emergence and transmission.” Toward this, Borges et al [ 9 ] have created INSaFLU, a bioinformatics platform for influenza virus sequencing data aimed toward increasing accessibility and analytical standardization across global research efforts. Such initiatives to maximize data and resource sharing to facilitate infectious disease research extend well beyond influenza—not only does the siRNA screening system presented by Hoenen and colleagues [ 5 ] provide the community with a minable dataset related to Ebola pathogenesis, but it also offers an experimental system that does not require biosafety level (BSL)-4, allowing similar experiments to be undertaken by many more labs.…”