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

Abstract

A 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 wholegenome 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. Here, we launch INSaFLU ("INSide the FLU"), which, to the best of our knowledge, is the first influenza-specific bioinformatics free web-based suite that deals with primary 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 advanced, multi-step software intensive analyses in a userfriendly manner without previous training in bioinformatics. INSaFLU gives access to user-restricted sample databases and projects' management, being a transparent and highly flexible tool specifically designed to automatically update project outputs as more samples are uploaded. Data integration is thus completely 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-tune 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. In 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 WGSbased surveillance for influenza virus.INSaFLU can be accessed through https://insaflu.insa.pt (see homepage view in Figure 1).

SARS-CoV-2 Whole Genome Amplification and Sequencing for Effective Population-Based Surveillance and Control of Viral Transmission

“…https://www.genomedetective.com/app/typingtool/virus/) (7,8) which can take raw sequencing (Fastq) files to assemble viral genomes, and to perform multiple sequence alignment and phylogenetic analysis for virus origin tracking. In addition, the described approach does not require significant data storage or computational investment as shown by our cost, data, and scalability calculations ( Table 3).…”

“…https://www.genomedetective.com/app/typingtool/virus/) (7,8), composed of the above tools, can be used to generate viral sequences from raw Fastq data.…”

Comparison of SARS-CoV-2 variants with INSaFLU and galaxyproject

“…Although we tried to use all of the samples that were already analyzed in the Galaxy workflow, it was not possible to upload some of the samples because of technical limitations. Due to the lack of time, limited storage, computer pace and internet connection capacities, we used the web interface of INSaFLU workflow (Borges, Pinheiro, Pechirra, Guiomar, & Gomes, 2018) which allows the upload of files smaller than 300 Mb. Because of the file size limitation, we could not try all samples with INSaFLU, while the large size samples have been analyzed in Galaxy workflow.…”

Influenza Classification from Short Reads with VAPOR Facilitates Robust Mapping Pipelines and Zoonotic Strain Detection for Routine Surveillance Applications

“…Whilst methodologies exist involving passage of isolates, sequencing can be performed directly from clinical swabs with single-reaction genomic reverse transcription polymerase chain reaction (RT-PCR) [16] [12]. Furthermore, bioinformatics pipelines have begun to be developed for efficient processing of this data [17] [18].…”