Developing a reproducible bioinformatics workflow for canine inherited retinal disease
Notice bibliographique
Résumé
Inherited Retinal Degenerations (IRDs) are a heterogenous group of diseases which lead to vision impairment and can be found both in humans and in dogs. About 1 in 1,380 humans is estimated to suffer from an autosomal recessive IRD, which would be 5.5 million people worldwide, and many more are estimated to be unaffected carriers. This makes autosomal recessive IRDs likely the most common group of Mendelian diseases in humans. Today, about 300 genetic mutations have been connected to cause retinal diseases in humans. Whilst in dogs only 32 genes have been identified, numerous eye conditions have been described where the genetic cause has not yet been identified. This suggests that there are much more genetic causes to discover in the dog genome. Additionally, the dog serves well as a model organism to investigate IRDs as it is sharing morphological and genetic similarities with humans. For these reasons, proper software, a canine reference genome of high quality, and smart implementation of bioinformatic tools and methods are a big advantage to increase chances of finding new causative genetic variants and subsequently enable faster detection of possible preventions of the disease or at least alleviating its symptoms via early diagnosis. In this project, a pre-existing pipeline consisting of Bash scripts was stepwise improved with the goal to increase its efficiency. After controlling whether previous data could still be reproduced with the old pipeline in a first step, the software was exchanged to more updated versions in a second step. A main change was the replacement of the mapping tool Burrows-Wheeler Aligner (BWA) from bwa mem to bwa-mem2 mem, and the update of deprecated Genome Analysis Toolkit (GATK) 3.7 to version 4.3 or 4.4. Thirdly, the scripts were adapted from using the older canine reference genome CanFam3.1 to CanFam4. In a fourth step, for automatization and fastening the running time, the pipeline steps were implemented into the workflow management system Nextflow. Additionally, this step was partly aiming to make the pipeline in concordance with the FAIR-principles. All steps were tested on the same test data set, a Labrador retriever family trio, in which one genetic cause for a canine form of the IRD Stargardt disease in a previous study had been detected, namely an insertion in the ABCA4 gene. Lastly, the workflow was also tested on a second data set of a novel IRD of unknown genetic origin on two sibling pairs of Chinese Crested Dogs (CCR). The adjustment of the pipeline shows similar results regarding the change of mapping tool. Introducing the new reference genome revealed a drop of average coverage by one read average for when using CanFam4, while other results were similar. Using the new reference genome increased the number of unknown variants compared to findings with CanFam3.1. However, the known causative variant for the canine form of Stargardt disease, an insertion in ABCA4 gene, could be found in all cases. The run with Nextflow produced identical results to when the respective steps were run with Bash scripts, but it reduced the running time. Running the workflow on the new data set (CCR) and subsequent annotation and filtering indicate new candidates which could be further investigated as a potential cause for this currently unknown cause for an IRD.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,005 | 0,002 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,020 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».