Student Frequently Asked Questions

The following information was cultivated from students’ Frequently Asked Questions (FAQs) about the general processes and key ideas for eukaryotic gene annotation. Special thanks to GEP TA D’Andrew Harrington (College of Southern Nevada) for creating the original version of this living document.

GEP UCSC Genome Browser

If you bump into a situation where your gene annotation contains many in-frame stop codons or even if your initial CDS doesn’t appear to have a start Methionine, more than likely, you are examining your gene in the wrong orientation.

      1. Your BLAST results should indicate if your query is expected to be on the positive or negative strand. Be sure to read your BLAST report thoroughly.
      2. If your gene is on the negative strand, click on the “reverse” button to flip the gene’s orientation so it still reads from left-to-right. The figure below shows what this might look like when examining your first and second CDSs.

We see CDS1 and CDS 2 in the incorrect orientation. Can you spot the problems with this reading?

Notice in the picture above of CDS1 and CDS 2, we see two crucial errors in our reading:

We do not see a start Methionine (ATG) in CDS1.

Each frame in CDS2 has multiple in-frame stop codons.

We see CDS1 and CDS2 in the reverse orientation. Can you spot why this is correct as compared to the above figure?

It’s okay if the gene is on opposite strands in D. melanogaster and your ortholog. Ensure the rest of the genomic neighborhood makes sense (i.e., adjacent genes on opposite strands in D. melanogaster should also be on opposite strands in your ortholog). In an ideal world, the relative orientation and placement of genes should be consistent.

It depends. Tracks have been designed within the GEP UCSC Genome Browser based on the kind of research being done. It is essential to ask the question, “What information am I looking for that the Genome Browser can provide to me?” Examples could be RNA-Seq data, in-frame stop codons, comparative genomics across a genus, or many other things. In general, though, we recommend selecting your “Default Tracks” to get started. Aside from this, we have several other tracks that will stand out during your annotation work, see below:

  • Mapping and Sequencing Tracks → Base Position → FULL
  • Genes and Gene Prediction Tracks → FlyBase Genes → PACK
  • RNA Seq Tracks → FlyBase Exon Junctions → PACK
  • Updated Transcriptome Tracks → Splice Junctions (discretionary) → PACK
  • Comparative Genomics tracks (if available)

At first glance, it can be challenging to know what nucleotide position the GEP UCSC Genome Browser is showing you. A useful method of reading this frame can be found by looking for the pipe, denoted with this “|” symbol. By looking for the number in between each pipe, you can quickly figure out which nucleotide corresponds with the overall scaffold count.

We can see the start of CDS1 and how each position number is enclosed in pipes. We color coded these pipes to have a better look.

Absolutely! You can increase the font size by clicking on “configure” and then selecting a different “text size.” This font size can be adjusted to many different sizes – so pick whichever you find most comfortable.

We see the configure button located on the universal ribbon panel.

Underneath configure, we can see numerous different options, including text size.

BLAST: Basic Local Alignment Search Tool

BLAST, known as the Basic Local Alignment Search Tool, can identify similarities or differences within biological data. It accomplishes this by providing the user an E-value (Expected Value) to determine statistical significance. Explanations of each of the five types of BLAST and their uses can be found below:

BLAST
Type
Query
(sequence to match)
Database/Subject (searching for match)FunctionUse Cases
blastn (nucleotide)nucleotidenucleotidesearching with shorter queries, cross-species comparisonmap mRNAs against genomic assemblies
blastp (protein)proteinproteingeneral sequence identification and
similarity searches
search for proteins similar to predicted genes
blastxnucleotide → proteinproteinidentifying potential protein products encoded by a nucleotide querymap proteins/CDS against genomic sequence
tblastnproteinnucleotide → proteinidentifying database sequences encoding proteins similar to querymap proteins against genomic assemblies
tblastxnucleotide → proteinnucleotide → proteinidentifying nucleotide sequences similar to the query based on their coding potential 
identify genes in unannotated sequences

Arrows indicate the BLAST program translates the nucleotide sequence before performing the search.

Each BLAST tool has a different function. You should understand that various tools of BLAST will provide you with different outcomes. If you use the wrong tool, you should expect information that doesn’t make sense and precious time lost. See the table below to see examples of how each tool of BLAST can be used in relation to your time with the GEP. If you ever see results that do not make sense, feel free to reach out to the GEP TA’s, and we will be more than happy to provide more in-depth explanations and assistance with your queries.

BLAST TypeDescription of Usage as a Scientific Question
blastn“Are there nucleotide similarities from D. melanogaster to D. yakuba?”
blastp“Are there peptide similarities from D. melanogaster to D. yakuba?”
blastx“Are there peptide similarities inside of D. melanogaster that I can find with only my nucleotide sequence from D. yakuba?”
tblastn“Are there nucleotide similarities inside of D. melanogaster that I can find with only my peptide sequence from D. yakuba?”
tblastx“Are there translated nucleotide similarities from D. melanogaster that are found in the translated nucleotides of D. yakuba?”

BLAST has three methods for the GEP to understand a target species and our reference species D. melanogaster. Instead of searching broad areas (all NCBI genomes) that may have no information pertaining to your query, BLAST can also be used for more narrow searches such as for Entrez and Assembly searches. It is easy to understand these different search queries as an overlapping funnel that varies based on what you are searching for with BLAST. The figures below show each query type and how they relate to the search results.

An inverted pyramid showing each search type can be considered a subset of the prior. Notice the overall size change is based on your search area.

The differences between Broad (All NCBI), Entrez, and Assembly searches can best be seen like funnels.

Reading the BLAST results page can be daunting at first. Be sure to anchor yourself to your initial scientific question so you don’t get lost. For a detailed breakdown of reading BLAST results, refer to the GEP Tools | NCBI BLAST video tutorial. Below is an example of a BLAST results page and a legend with specific explanations for how to interpret the page.

A BLAST search results page for the target species D. yakuba.

Legend key to interpret the BLAST search results page shown above.

The RefSeq protein track found in the GEP UCSC Genome Browser is linked to RefSeq: NCBI Reference Sequence Database. When given a REFSEQ protein track, we need to ask the questions, “What is the predicted function of this gene?” and “Does this predictive function match what is found in D. melanogaster?” This, in turn, will give us a better scope of if we have located our target D. melanogaster homolog.

With a peptide sequence or even the Protein ID, we will need to use blastp. The tricky part of searching for a predictive function is that we don’t want to search for everything since this will take far too long and render many useless results. Consider the following questions:

  • “What kind of input x and output f(x) am I expecting?”
    • Example: “I have a Protein sequence and am looking for Protein returns.”
  • “Do I need results from hits outside of my genus, within the genus, or a specific species within my genus?”
    • Example: “I have a Protein sequence from D. kikkawai, and I am trying to find its predictive function from D. melanogaster.”


Assuming the answers to our questions above are “blastp” and “within the genus,” we can run this report by calling on an Entrez search with D. melanogaster (taxid: 2772).

“Choose Search Set” box found in all BLAST toolkits.

In the Database drop-down menu, be sure you are selecting the correct database—“Reference proteins.” Notice that “refseq_protein” is shorthand for this database.

Typically, you’ll search with the reference species in the Organism field set to “Drosophila melanogaster.”

BLAST relies on statistical analysis to determine areas between inputs and databases to produce statistically significant results. Sometimes when inputting a specific coding DNA sequence (CDS) or query, it is possible that BLAST will not warrant any results. This can happen for numerous reasons, three of the most common are listed below:

  • No significant results are found. As simple as it sounds, sometimes, through enough evolution, CDSs can drastically change from one species to the next. If your species is several steps away from D. melanogaster, this CDS may be drastically different.
    • NOTE: When examining this kind of change, you can use the “Comparative Genomic” tabs in the GEP UCSC Genome Browser to provide evidence of this. Don’t expect to jump to this conclusion immediately – we are scientists and want to see why we are getting this result.
  • The query submitted may not have been submitted correctly. Be sure to look back at which BLAST tool you are using and exam the query sequence you are comparing to it.
  • Sometimes the information you are searching for can be “lost in translation.” This can cause problems with several search parameters, and BLAST ends up providing too little or too much and simply doesn’t want to flood you with information. To fix this kind of error, we suggest refining your search. A further explanation of refining your CDS can be found in the GEP | Pathways Annotation – CDS Annotation and Refinement video.

The short answer is yes, BLAST is essential. Many computational biologists, geneticists, and numerous other scientists use BLAST day-to-day for complex calculations and analysis. Without BLAST, we would not be able to accomplish most of what is done during Bioinformatic analysis. Could you imagine having to calculate these E-values and Alignments by hand? It isn’t fun. Remember that BLAST is a tool, and like all tools across industries, it is better to stay ahead of the curve and be confident in your ability to use this widely demanded tool.

An e-value, also known as an expected value, is a value provided by BLAST to denote expected random chance when searching the database with your query sequence. The closer your e-value to zero, the less noise we expect to see and the more “significant” our match becomes. Just because we get an e-value of 0.0, doesn’t mean it is perfect though. Let’s examine two situations where an e-value would be 0.0 for different reasons:

  • From BLAST, an e-value of 0.0 can occur when we examine short sequences against other short databases that are near identical. According to NCBI, “shorter sequences have a higher probability of occurring in the database purely by chance.” As scientists and annotators, we do not look for chance, we look for evidence to form solid hypotheses and ideas.
  • From a computer science perspective, systems hold values as integers and floating point numbers (has a decimal point). When dividing or calculating chances and limits, these floating points, much like on a calculator, have a set amount of space. Because all computers contain what is known as a “Floating-Point Error,” BLAST mitigates this error by rounding to an e-value of 0.0 when our value is < 5*10e-324. To put that into perspective, 5*10e-324 looks like this:

0.00000000000000000000000000000000000000000000000000000000000000000000000 0000000000000000000000000000000000000000000000000000000000000000000000000 0000000000000000000000000000000000000000000000000000000000000000000000000 0000000000000000000000000000000000000000000000000000000000000000000000000 00000000000000000000000000000000005.

    • Simply put, it is because our design of computers in bits is in a Log2 format, this kind of floating-point error is here to stay for a while.
  • Overall, e-values that approach 0.0 or 0e0 are far lower than e-values approaching 1. Always think in terms of “noise.” The less noise, the better and more confident we can be in our results.
BLAST results within the GEP should not take several hours. Based on our query sizes, you should expect search times of between seconds to several minutes only. If you are getting search times of several hours, we have a few suggestions for this:
  • Review what query sequence you have submitted and against what database.
    • If the incorrect database or query is submitted, BLAST can take much longer to fulfill your request. Take a moment to review your query and databases and run them again.
  • Peak Times occur within BLAST. It is best to run a BLAST search on a different internet browser tab at specific peak times. This is due to BLAST being a cloud computing service used by thousands of scientists daily. Being one of those scientists, you will experience the very same delay.
    • These peak times vary, but BLAST will indicate when they occur.

For more information on troubleshooting your BLAST results and why these results may be taking so long, feel free to reach out to any of the GEP Virtual TAs or see NCBI’s troubleshooting page.

F-Element Project

You may find genes in the contig of your target species that do not match what you expect. This is normal and requires some investigating of your contig to ensure you’re reading the results accurately.

  • Ensure your target species coordinates
    • This can often happen, which is okay. Always be sure to double-check your location to make sure you are on the correct species and contig.
  • Is the gene a known transposon?
    • As the name suggests, transposons are possible, so be sure to investigate your genes in D. melanogaster to determine if one may be present.
  • Do the large exons searches still warrant no results, even with a smaller word search?
    • If your adjusted BLAST searches are still not showing any statistically significant results, we are more likely to see a false flag.
  • Does a gene in the same family align better than the predicted gene?
    • For genes, one size does not fit all. In this case, check other in-family genes to determine if there is a better result or match.

When looking at a contig and you are running an instance where a predicted gene is coming back with either: a) low RNA-Seq with high BLAST predictions or b) high RNA-Seq with low BLAST predictions. This can occur with what’s known as a pseudogene, however, it is incredibly rare. It can also occur when the region we are annotating is a repeating sequence elsewhere in the genome; the RNA-Seq might have misaligned at an incorrect location.

  • They are more common in the F-Element Project than the Pathways and Parasitoid Wasps Projects due to the resolution at which we annotate.
  • The resolution at which we annotate can detect fluctuating RNA-Seq data with high predictions or exactly the opposite. When these situations do occur, it is good to review this area extensively as to rule out the possibility of consensus error, polymorphisms from our target species with D. melanogaster, or even a gene has yet to be annotated. Regardless, it is good practice to exhaust our annotation tools when we come across these situations, because as scientists we must have evidence to support our annotations as a whole.

Pathways Project

In the Pathways Project, you will need to keep critical pieces of information on standby. There will be times where you will not finish an annotation or will need to navigate through multiple tabs at any given time. Document the following for quicker navigation:

The Gene Record Finder uses FlyBase to collect the most pertinent information you will need during your time annotating your target gene. Because of how this webpage interacts with the FlyBase records, you must type the target gene symbol precisely as indicated, as they are case-sensitive. Even if one capital letter, one dash, or even a single character isn’t exactly as it is found on FlyBase, you might get an error that looks like the figure below:

While pten is a real gene, the correct entry would be “Pten” not “pten,” otherwise you’ll see the error message above.