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How to identify ortholog sequences in non-model organisms not present on NCBI/UniProt?

Hi everyone,

I am a master's student in biology and I am working on a mostly bioinformatic thesis, however I am not extremely proficient in coding, besides having a bunch of courses during my bachelor's and masters. For now, the idea of my project is to first collect as many mammalian ortholog sequences for a certain protein from different taxa and then correlate changes in previously identified amino acid repeat regions to like body size and maybe other adaptations afterwards. As a first step for collecting sequences I wrote a python script using requests library to query UniProt, NCBI and DNAZoo for existing all ortholog sequences present in these databases. It gives me out one fasta file with all sequences for each taxon I am interest in looking at. To check if the sequences were complete I used the fasta file generated by my pipeline into a MSA web service. Of course, many coming from DNAZoo are missing big chunks of their sequence. Hence, I decided to use QuickProt to do protein-to-genome alignment and coding sequence prediction for those incomplete sequences. For some cases, QuickProt gave much better and results, as sequences seem more complete and align well with others from better annotated genomes.

However, is my procedure sensible, and should I validate/verify further the results from QuickProt?

Thanks in advance for the help

ncbi orthologs

1 answer

Your academic supervisor should be your first port of call for questions like this.

This question is a little hard to answer here because we don't know what you intend to do with said orthologs, so we don't know how mis-annotations or poor quality data will impact downstream analysis. DNAZoo, as you have suggested doesn't offer the highest quality data, which makes direct comparisons to curated high-quality data from UniProt difficult, for example.

Conceptually, what you've done seems logical, but mixing data from multiple sources can come with problems for interpretation. I would suggest more validation from QuickProt and DNAZoo data would be useful, but how far you go depends on what you want from the project.

Hi, thank you for taking your time to help me out.

Within this protein they identified regions with amino acid repeats whose number vary between organisms and seems to correlate with body size. Hence why I am gathering many sequences from species of different taxa like primates, rodents, carnivora with the idea to then carry out MSA to identify these regions and count the number of repeating amino acids in each species and correlate it to body size and maybe other sensible adaptations.

About validating the sequences predicted with QuickProt, I was thinking about creating a phylogenetic tree with the QuickProt predictions and with sequences from RefSeq genomes. My idea was that, say I get 10 new primate sequences, I would then create a phylogenetic tree with a few curated sequences (Human, Rhesus Monkey, Mice, Rat and others from other taxonomic groups). My expectation would be that if the predictions are correct, they would cluster with those from the same taxonomic group. However, I am also skeptical about validating them this way. Because I am using the same sequences (human and rhesus monkey) for protein-to-genome alignment and the gene itself is quite conserved across organisms, hence it seems almost obvious that they are bound to cluster together.

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