Model: raphassaraf/MNLP_M3_document_encoder-all-MiniLM-L6-v2
Author: raphassaraf
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:96, loss:MultipleNegativesRankingLoss, arxiv:1908.10084, arxiv:1705.00652, base_model:BAAI/bge-small-en-v1.5, base_model:finetune:BAAI/bge-small-en-v1.5, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: BAAI/bge-small-en-v1.5
Card metadata: tags: sentence-transformers, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:96, loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-small-en-v1.5
widget: source_sentence: What are the de facto required fields in a SAM/BAM read group?
sentences: Question: Several gene set enrichment methods are available, the most famous/popular is the Broad Institute tool. Many other tools are available (See for example the biocView of GSE which list 82 different packages). There are several parameters in consideration :

the statistic used to order the genes, 
if it competitive or self-contained,
if it is supervised or not,
and how is the enrichment score calculated.

I am using the fgsea - Fast Gene Set Enrichment Analysis package to calculate the enrichment scores and someone told me that the numbers are different from the ones on the Broad Institute despite all the other parameters being equivalent.
Are these two methods (fgsea and Broad Institute GSEA) equivalent to calculate the enrichment score?
I looked to the algorithms of both papers, and they seem fairly similar, but I don't know if in real datasets they are equivalent or not.
Is there any article reviewing and comparing how does the enrichment score method affect to the result?

Answer: According to the FGSEA preprint:

We ran reference GSEA with default parameters. The permutation number
  was set to 1000, which means that for each input gene set 1000
  independent samples were generated. The run took 100 seconds and
  resulted in 79 gene sets with GSEA-adjusted FDR q-value of less than
  10−2. All significant gene sets were in a positive mode. First, to get
  a similar nominal p-values accuracy we ran FGSEA algorithm on 1000
  permutations. This took 2 seconds, but resulted in no significant hits
  due after multiple testing correction (with FRD ≤ 1%).

Thus, FGSEA and GSEA are not identical.
And again in the conclusion:

Consequently, gene sets can be ranked more precisely in the results
  and, which is even more important, standard multiple testing
  correction methods can be applied instead of approximate ones as in
  [GSEA].

The author argues that FGSEA is more accurate, so it can't be equivalent.
If you are interested specifically in the enrichment score, that was addressed by the author in the preprint comments:

Values of enrichment scores and normalized enrichment scores are the
  same for both broad version and fgsea.

So that part seems to be the same., Question: I am running samtools mpileup (v1.4) on a bam file with very choppy coverage (ChIP-seq style data). I want to get a first-pass list of positions with SNVs and their frequency as reported by the read counts, but no matter what I do, I keep getting all SNVs filtered out as not passing QC.
What's the magic parameter set for an initial list of SNVs and frequencies?
EDIT: this is a question I posted on "the other" website, but didn't get a reply there.

Answer: I used this in the past for ChIP-seq data and it generated SNVs:
samtools mpileup \
--uncompressed --max-depth 10000 --min-MQ 20 --ignore-RG --skip-indels \
--fasta-ref ref.fa file.bam \
| bcftools call --consensus-caller \
> out.vcf

This was samtools 1.3 in case that makes a difference., Question: The SAM specification indicates that each read group must have a unique ID field, but does not mark any other field as required. 
I have also discovered that htsjdk throws exceptions if the sample (SM) field is empty, though there is no indication in the specification that this is required. 
Are there other read group fields that I should expect to be required by common tools? 

Answer: The sample tag (i.e. SM) was a mandatory tag in the initial SAM spec (see the .pages file; you need a mac to open it). When transitioned to Latex, this requirement was mysteriously dropped. Picard is conforming to the initial spec. Anyway, the sample tag is important to quite a few tools. I would encourage you to add it., source_sentence: Is the optional SAM NM field strictly computable from the MD and CIGAR?
sentences: Question: I'm looking for tools to check the quality of a VCF I have of a human genome. I would like to check the VCF against publicly known variants across other human genomes, e.g. how many SNPs are already in public databases, whether insertions/deletions are at known positions, insertion/deletion length distribution, other SNVs/SVs, etc.? I suspect that there are resources from previous projects to check for known SNPs and InDels by human subpopulations.
What resources exist for this, and how do I do it? 

Answer: To achieve (at least some of) your goals, I would recommend the Variant Effect Predictor (VEP). It is a flexible tool that provides several types of annotations on an input .vcf file.  I agree that ExAC is the de facto gold standard catalog for human genetic variation in coding regions.  To see the frequency distribution of variants by global subpopulation make sure "ExAC allele frequencies" is checked in addition to the 1000 genomes. 
Output in the web-browser:

If you download the annotated .vcf, frequencies will be in the INFO field:
##INFO=<ID=CSQ,Number=.,Type=String,Description="Consequence annotations from Ensembl VEP. Format: Allele|Consequence|IMPACT|SYMBOL|Gene|Feature_type|Feature|BIOTYPE|EXON|INTRON|HGVSc|HGVSp|cDNA_position|CDS_position|Protein_position|Amino_acids|Codons|Existing_variation|DISTANCE|STRAND|FLAGS|SYMBOL_SOURCE|HGNC_ID|TSL|SIFT|PolyPhen|AF|AFR_AF|AMR_AF|EAS_AF|EUR_AF|SAS_AF|AA_AF|EA_AF|ExAC_AF|ExAC_Adj_AF|ExAC_AFR_AF|ExAC_AMR_AF|ExAC_EAS_AF|ExAC_FIN_AF|ExAC_NFE_AF|ExAC_OTH_AF|ExAC_SAS_AF|CLIN_SIG|SOMATIC|PHENO|MOTIF_NAME|MOTIF_POS|HIGH_INF_POS|MOTIF_SCORE_CHANGE

The previously mentioned Annovar can also annotate with ExAC allele frequencies.  Finally, should mention the newest whole-genome resource, gnomAD., Question: I produced a bam file by aligning reads to a small set of synthetic sequences using bwa-mem.
I am heavily filtering reads that are not paired and of a certain orientation.
Applying the filtering, I get a few thousands of reads:
samtools view -h $myfilebam | \
samtools view -h -F4 - | \
samtools view -h -F8 - | \
samtools view -h -F256 - | \
samtools view -h -F512 - | \
samtools view -h -F1024 - | \
samtools view -h -F2048 - | \
samtools view -h -f16 - | \
samtools view -h -f32 -  | wc -l

Gives me 89502 reads.
If I then pipe this into samtools mpileup, I get no results:
samtools view -h $myfilebam | \
samtools view -h -F4 - | \
samtools view -h -F8 - | \
samtools view -h -F256 - | \
samtools view -h -F512 - | \
samtools view -h -F1024 - | \
samtools view -h -F2048 - | \
samtools view -h -f16 - | \
samtools view -h -f32 -  | \
samtools mpileup --excl-flags 0 -Q0 -B -d 999999 - | wc -l

Returns 0.
I tried different combinations of filtering, and when I do both -f 16 and -f 32 returns empty, but if I do either of those, then it works:
samtools view -h $myfilebam | \
samtools view -h -F4 - | \
samtools view -h -F8 - | \
samtools view -h -F256 - | \
samtools view -h -F512 - | \
samtools view -h -F1024 - | \
samtools view -h -F2048 - | \
samtools view -h -f16 - | \
samtools mpileup --excl-flags 0 -Q0 -B -d 999999 - | wc -l

Returns 1056.
Any ideas why? My thinking was that it would work with --excl-flags 0.
EDIT: substituting mpileup for depth does work, and prints out each position and the depth as expected.
EDIT2: adding -q 0 to mpileup gives the same empty result.
Thanks in advance

Answer: By using -h in the samtools view command, you're including all the header lines in your word count. If you happen to have about 89500 reference sequences, then the lengths of those would all appear in the header and inflate the -h word count, but not the mpileup count. Try piping it through an additional samtools view (i.e. without -h) and see if the counts change:
...
samtools view -h -f32 -  | \
samtools view | wc -l

Also, samtools mpileup by default only considers high-quality bases and concordant reads. Try adding a -A to your mpileup line (which stops anomalous read pairs from being discarded):
...
samtools mpileup -A -Q0 -B -d 999999 - | wc -l

Whether or not this is actually a good idea will be dependent on what you want to get out of the analysis, and what the downstream programs / analyses are expecting., Question: From SAM Optional Fields Specification the NM field is 

Edit distance to the reference, including ambiguous bases but excluding clipping

Assuming both the MD and CIGAR are present, is the edit distance simply the number of characters [A-Z] appearing in the MD field plus the number of bases inserted (xI, if any) from the CIGAR string? Are there any other complications? 

Answer: Assuming both the MD and CIGAR are present and correct, then yes, you can parse both to get the edit distance (NM auxiliary tag). One big caveat to this is that there's a reason that the samtools calmd command exists, since it's historically been the case that not all aligners have output correct MD strings. It's rare for the CIGAR string to be wrong and that'd be more of a catastrophic error on the part of an aligner. For what it's worth, if the NM auxiliary is absent on a given alignment but present on others produced by the same aligner then it's fair to assume NM:i:0 for a given alignment by default (many aligners only produce NM:i:XXX if the edit distance is at least 1)., source_sentence: How to read structural variant VCF?
sentences: Question: I am calling SNPs from WGS samples produced at my lab. I am currently using bwa-mem for mapping Illumina reads as it is recommended by GATK best practice. However, bwa is a bit slow. I heard from my colleague that SNAP is much faster than bwa. I tried it on a small set of reads and it is indeed faster. However, I am not sure how it works with downstream SNP callers, so here are my questions: have you used SNAP for short-read mapping? What is your experience? Does SNAP work well with SNP callers like GATK and freebayes? Thanks!

Answer: GATK best practices are explicably meant to consume BWA MEM generated BAM.  Whilst SNAP may be faster, the Broad will not have tested it for compatibility with GATK as such you can't guaranty using it won't have unexpected consequences.  
As such you'd be better off using BWA MEM because I assume accurately called variation is always better than fast and incorrectly called variation.  The main issue you'll have is ensuring shorter split hits and mapping quality are reported in the same way as bwa MEM -M which GATK/Picard is expecting.  Ultimately however you'd be better off posting this question on the GATK forum. 
It's also worth noting that the soon to be released GATK 4 will utilise bwaspark which can distribute it's alignment processes across Apache Spark for increase performance.  Consequently I can't see SNAP being adopted anytime soon., Question: I have a computer engineering background, not biology.
I started working on a bioinformatics project recently, which involves de-novo assembly. I came to know the terms Transcriptome and Genome, but I cannot identify the difference between these two.
I know a transcriptome is the set of all messenger RNA molecules in a cell, but am not sure how this is different from a genome.

Answer: In brief, the  “genome”  is the collection of all  DNA  present  in  the  nucleus  and  the  mitochondria of a  somatic  cell. The initial product of genome expression is the “transcriptome”, a collection of RNA molecules derived from those genes., Question: The IGSR has a sample for encoding structural variants in the VCF 4.0 format.
An example from the site (the first record):
#CHROM  POS   ID  REF ALT   QUAL  FILTER  INFO  FORMAT  NA00001
1 2827693   . CCGTGGATGCGGGGACCCGCATCCCCTCTCCCTTCACAGCTGAGTGACCCACATCCCCTCTCCCCTCGCA  C . PASS  SVTYPE=DEL;END=2827680;BKPTID=Pindel_LCS_D1099159;HOMLEN=1;HOMSEQ=C;SVLEN=-66 GT:GQ 1/1:13.9

How to read it? From what I can see:

This is a deletion (SVTYPE=DEL)
The end position of the variant comes before the starting position (reverse strand?)
The reference starts from 2827693 to 2827680 (13 bases on the reverse strand)
The difference between reference and alternative is 66 bases (SVLEN=-66)

This doesn't sound right to me. For instance, I don't see where exactly the deletion starts. The SVLEN field says 66 bases deleted, but where? 2827693 to 2827680 only has 13 bases between.
Q: How to read the deletion correctly from this structural VCF record? Where is the missing 66-13=53 bases?

Answer: I just received a reply from 1000Genomes regarding this. I'll post it in its entirety below:

Looking at the example you mention, I find it difficult to come up with an
  interpretation of the information whereby the stated end seems to be correct,
  so believe that this may indeed be an error.
Since the v4.0 was created, however, new versions of VCF have been introduced,
  improving and correcting the specification. The current version is v4.3
  (http://samtools.github.io/hts-specs/). I believe the first record shown on
  page 11 provides an accurate example of this type of deletion.
I will update the web page to include this information.

So we can take this as official confirmation that we were all correct in suspecting the example was just wrong., source_sentence: Publicly available genome sequence database for viruses?
sentences: Question: This question is based on a question on BioStars  posted >2 years ago by user jack.
It describes a very frequent problem of generating GO annotations for non-model organisms. While it is based on some specific format and single application (Ontologizer), it would be useful to have a general description of the pathway to getting to a GAF file. 
Note, that the input format is lacking a bit of essential information, like how it was obtained. Therefore, it is har to assign evidence code. Therefore, lets assume that the assignments of GO terms were done automagically. 

I want to do the Gene enrichment using Ontologizer without a
  predefined association file(it's not model organism). 
I have parsed a file with two columns for that organism like this : 
geneA  GO:0006950,GO:0005737
geneB  GO:0016020,GO:0005524,GO:0006468,GO:0005737,GO:0004674,GO:0006914,GO:0016021,GO:0015031
geneC  GO:0003779,GO:0006941,GO:0005524,GO:0003774,GO:0005516,GO:0005737,GO:0005863
geneD  GO:0005634,GO:0003677,GO:0030154,GO:0006350,GO:0006355,GO:0007275,GO:0030528

I have downloaded the .ob file from Gene ontology file which contain
  this information (from here) : 
!
! GO IDs (primary only) and name text strings
! GO:0000000 [tab] text string [tab] F|P|C
! where F = molecular function, P = biological process, C = cellular component
!
GO:0000001  mitochondrion inheritance   P
GO:0000002  mitochondrial genome maintenance    P
GO:0000003  reproduction    P
GO:0000005  ribosomal chaperone activity    F
GO:0000006  high affinity zinc uptake transmembrane transporter activity    F
GO:0000007  low-affinity zinc ion transmembrane transporter activity    F
GO:0000008  thioredoxin F
GO:0000009  alpha-1,6-mannosyltransferase activity  F
GO:0000010  trans-hexaprenyltranstransferase activity   F
GO:0000011  vacuole inheritance P

What I need as output is .gaf file in the following format (in the
  format of the files here):
!gaf-version: 2.0

!Project_name: Leishmania major GeneDB

!URL: http://www.genedb.org/leish

!Contact Email: mb4@sanger.ac.uk

 GeneDB_Lmajor    LmjF.36.4770    LmjF.36.4770        GO:0003723    PMID:22396527    ISO    GeneDB:Tb927.10.10130    F    mitochondrial RNA binding complex 1 subunit, putative    LmjF36.4770    gene    taxon:347515    20120910    GeneDB_Lmajor       
 GeneDB_Lmajor    LmjF.36.4770    LmjF.36.4770        GO:0044429    PMID:20660476    ISS        C    mitochondrial RNA binding complex 1 subunit, putative    LmjF36.4770    gene    taxon:347515    20100803 GeneDB_Lmajor             GeneDB_Lmajor    LmjF.36.4770    LmjF.36.4770        GO:0016554    PMID:22396527    ISO    GeneDB:Tb927.10.10130    P    mitochondrial RNA binding complex 1 subunit, putative    LmjF36.4770    gene   taxon:347515    20120910    GeneDB_Lmajor       
 GeneDB_Lmajor    LmjF.36.4770    LmjF.36.4770        GO:0048255    PMID:22396527    ISO    GeneDB:Tb927.10.10130    P    mitochondrial RNA binding complex 1 subunit, putative    LmjF36.4770    gene    taxon:347515    20120910    GeneDB_Lmajor  

How to create your own GO association file (gaf)?

Answer: Here's a Perl script that can do this:
#!/usr/bin/env perl 
use strict;
use warnings;

## Change this to whatever taxon you are working with
my $taxon = 'taxon:1000';
chomp(my $date = `date +%Y%M%d`);

my (%aspect, %gos);
## Read the GO.terms_and_ids file to get the aspect (sub ontology)
## of each GO term. 
open(my $fh, $ARGV[0]) or die "Need a GO.terms_and_ids file as 1st arg: $!\n";
while (<$fh>) {
    next if /^!/;
    chomp;
    my @fields = split(/\t/);
    ## $aspect{GO:0000001} = 'P'
    $aspect{$fields[0]} = $fields[2];
}
close($fh);

## Read the list of gene annotations
open($fh, $ARGV[1]) or die "Need a list of gene annotattions as 2nd arg: $!\n";
while (<$fh>) {
    chomp;
    my ($gene, @terms) = split(/[\s,]+/);
    ## $gos{geneA} = (go1, go2 ... goN)
    $gos{$gene} = [ @terms ];
}
close($fh);

foreach my $gene (keys(%gos)) {
    foreach my $term (@{$gos{$gene}}) {
        ## Warn and skip if there is no aspect for this term
        if (!$aspect{$term}) {
            print STDERR "Unknown GO term ($term) for gene $gene\n";
            next;
        }
        ## Build a pseudo GAF line 
        my @out = ('DB', $gene, $gene, ' ', $term, 'PMID:foo', 'TAS', ' ', $aspect{$term},
                             $gene, ' ', 'protein', $taxon, $date, 'DB', ' ', ' ');
        print join("\t", @out). "\n";
    }
}

Make it executable and run it with the GO.terms_and_ids file as the 1st argument and the list of gene annotations as the second. Using the current GO.terms_and_ids and the example annotations in the question, I get:
$ foo.pl GO.terms_and_ids file.gos 
DB  geneD   geneD       GO:0005634  PMID:foo    TAS     C   geneD       protein taxon:1000  20170308    DB       
DB  geneD   geneD       GO:0003677  PMID:foo    TAS     F   geneD       protein taxon:1000  20170308    DB       
DB  geneD   geneD       GO:0030154  PMID:foo    TAS     P   geneD       protein taxon:1000  20170308    DB       
Unknown GO term (GO:0006350) for gene geneD
DB  geneD   geneD       GO:0006355  PMID:foo    TAS     P   geneD       protein taxon:1000  20170308    DB       
DB  geneD   geneD       GO:0007275  PMID:foo    TAS     P   geneD       protein taxon:1000  20170308    DB       
DB  geneD   geneD       GO:0030528  PMID:foo    TAS     F   geneD       protein taxon:1000  20170308    DB       
DB  geneB   geneB       GO:0016020  PMID:foo    TAS     C   geneB       protein taxon:1000  20170308    DB       
DB  geneB   geneB       GO:0005524  PMID:foo    TAS     F   geneB       protein taxon:1000  20170308    DB       
DB  geneB   geneB       GO:0006468  PMID:foo    TAS     P   geneB       protein taxon:1000  20170308    DB       
DB  geneB   geneB       GO:0005737  PMID:foo    TAS     C   geneB       protein taxon:1000  20170308    DB       
DB  geneB   geneB       GO:0004674  PMID:foo    TAS     F   geneB       protein taxon:1000  20170308    DB       
DB  geneB   geneB       GO:0006914  PMID:foo    TAS     P   geneB       protein taxon:1000  20170308    DB       
DB  geneB   geneB       GO:0016021  PMID:foo    TAS     C   geneB       protein taxon:1000  20170308    DB       
DB  geneB   geneB       GO:0015031  PMID:foo    TAS     P   geneB       protein taxon:1000  20170308    DB       
DB  geneA   geneA       GO:0006950  PMID:foo    TAS     P   geneA       protein taxon:1000  20170308    DB       
DB  geneA   geneA       GO:0005737  PMID:foo    TAS     C   geneA       protein taxon:1000  20170308    DB       
DB  geneC   geneC       GO:0003779  PMID:foo    TAS     F   geneC       protein taxon:1000  20170308    DB       
DB  geneC   geneC       GO:0006941  PMID:foo    TAS     P   geneC       protein taxon:1000  20170308    DB       
DB  geneC   geneC       GO:0005524  PMID:foo    TAS     F   geneC       protein taxon:1000  20170308    DB       
DB  geneC   geneC       GO:0003774  PMID:foo    TAS     F   geneC       protein taxon:1000  20170308    DB       
DB  geneC   geneC       GO:0005516  PMID:foo    TAS     F   geneC       protein taxon:1000  20170308    DB       
DB  geneC   geneC       GO:0005737  PMID:foo    TAS     C   geneC       protein taxon:1000  20170308    DB       
DB  geneC   geneC       GO:0005863  PMID:foo    TAS     C   geneC       protein taxon:1000  20170308    DB       

Note that this is very much a pseudo-GAF file since most of the fields apart from the gene name, GO term and sub-ontology are fake. It should still work for what you need, however., Question: As a small introductory project, I want to compare genome sequences of  different strains of influenza virus.
What are the publicly available databases of influenza virus gene/genome sequences?

Answer: There area few different influenza virus database resources:

The Influenza Research Database (IRD) (a.k.a FluDB - based upon URL)

A NIAID Bioinformatics Resource Center or BRC which highly curates the data brought in and integrates it with numerous other relevant data types

The NCBI Influenza Virus Resource

A sub-project of the NCBI with data curated over and above the GenBank data that is part of the NCBI

The GISAID EpiFlu Database

A database of sequences from the Global Initiative on Sharing All Influenza Data. Has unique data from many countries but requires user agree to a data sharing policy.

The OpenFluDB

Former GISAID database that contains some sequence data that GenBank does not have.

For those who also may be interested in other virus databases, there are:

Virus Pathogen Resource (VIPR)

A companion portal to the IRD, which hosts curated and integrated data for most other NIAID A-C virus pathogens including (but not limited to) Ebola, Zika, Dengue, Enterovirus, and Hepatitis C

LANL HIV database

Los Alamos National Laboratory HIV database with HIV data and many useful tools for all virus bioinformatics

PaVE: Papilloma virus genome database (from quintik comment)

NIAID developed and maintained Papilloma virus bioinformatics portal

Disclaimer: I used to work for the IRD / VIPR and currently work for NIAID., Question: I have a set of genomic ranges that are potentially overlapping. I want to count the amount of ranges at certain positions using R. 
I'm Pretty sure there are good solutions, but I seem to be unable to find them. 
Solutions like cut or findIntervals don't achieve what I want as they only count on one vector or accumulate by all values <= break.
Also countMatches {GenomicRanges} doesn't seem to cover it.
Probably one could use Bedtools, but I don't want to leave R.
I could only come up with a hilariously slow solution
# generate test data
testdata <- data.frame(chrom = rep(seq(1,10),10),
                       starts = abs(rnorm(100, mean = 1, sd = 1)) * 1000,
                       ends = abs(rnorm(100, mean = 2, sd = 1)) * 2000)

# make sure that all end coordinates are bigger than start
# this is a requirement of the original data
testdata <- testdata[testdata$ends - testdata$starts > 0,]

# count overlapping ranges on certain positions
count.data <- lapply(unique(testdata$chrom), function(chromosome){
    tmp.inner <- lapply(seq(1,10000, by = 120), function(i){
        sum(testdata$chrom == chromosome & testdata$starts <= i & testdata$ends >= i)
    })
    return(unlist(tmp.inner))
})

# generate a data.frame containing all data
df.count.data <- ldply(count.data, rbind)

# ideally the chromosome will be columns and not rows
t(df.count.data)

Answer: GenomicRanges::countOverlaps seems to be what you’re after:
position_range = GRanges(position$chrom, IRanges(position, position, width = 1))
ranges_at_position = countOverlaps(position_ranges, granges), source_sentence: samtools depth print out all positions
sentences: Question: I have around ~3,000 short sequences of approximately ~10Kb long. What are the best ways to find the motifs among all of these sequences? Is there a certain software/method recommended?
There are several ways to do this. My goal would be to:
(1) Check for motifs repeated within individual sequences
(2) Check for motifs shared among all sequences
(3) Check for the presence of "expected" or known motifs
With respect to #3, I'm also curious if I find e.g. trinucleotide sequences, how does one check the context around these regions?
Thank you for the recommendations/help!

Answer: For (3), this page has a lot of links to pattern/motif finding tools. Following through the YMF link on that page, I came across the University of Washington Motif Discovery section. Of these projection seemed to be the only downloadable tool. I find it interesting how old all these tools are; maybe the introduction of microarrays and NGS has made them all redundant.
Your sub-problem (2) seems similar to the problem I'm having with Nippostrongylus brasiliensis genome sequences, where I'd like to find regions of very high homology (length 500bp to 20kb or more, 95-99% similar) that are repeated throughout the genome. These sequences are killing the assembly.
The main way I can find these regions is by looking at a coverage plot of long nanopore reads mapped to the assembled genome (using GraphMap or BWA). Any regions with substantially higher than median coverage are likely to be shared repeats.
I've played around in the past with chopping up the reads to smaller sizes, which works better for hitting smaller repeated regions that are such a small proportion of most reads that they are never mapped to all the repeated locations. I wrote my own script a while back to chop up reads (for a different purpose), which produces a FASTA/FASTQ file where all reads are exactly the same length. For some unknown reason I took the time to document that script "properly" using POD, so here's a short summary:

Converts all sequences in the input FASTA file to the same length.
     Sequences shorter than the target length are dropped, and sequences longer
     than the target length are split into overlapping subsequences covering
     the entire range. This prepares the sequences for use in an
     overlap-consensus assembler requiring constant-length sequences (such as
     edena).

And here's the syntax:
$ ./normalise_seqlengths.pl -h
Usage:
    ./normalise_seqlengths.pl <reads.fa> [options]

  Options:
    -help
      Only display this help message

    -fraglength
      Target fragment length (in base-pairs, default 2000)

    -overlap
      Minimum overlap length (in base-pairs, default 200)

    -short
      Keep short sequences (shorter than fraglength), Question: Without going into too much background, I just joined up with a lab as a bioinformatics intern while I'm completing my masters degree in the field. The lab has data from an RNA-seq they outsourced, but the only problem is that the only data they have is preprocessed from the company that did the sequencing: filtering the reads, aligning them, and putting the aligned reads through RSEM. I currently have output from RSEM for each of the four samples consisting of: gene id, transcript id(s), length, expected count, and FPKM. I am attempting to get the FASTQ files from the sequencing, but for now, this is what I have, and I'm trying to get something out of it if possible.
I found this article that talks about how expected read counts can be better than raw read counts when analyzing differential expression using EBSeq; it's just one guy's opinion, and it's from 2014, so it may be wrong or outdated, but I thought I'd give it a try since I have the expected counts.
However, I have just a couple of questions about running EBSeq that I can't find the answers to:
1: In the output RSEM files I have, not all genes are represented in each, about 80% of them are, but for the ones that aren't, should I remove them before analysis with EBSeq? It runs when I do, but I'm not sure if it is correct.
2: How do I know which normalization factor to use when running EBSeq? This is more of a conceptual question rather than a technical question.
Thanks!

Answer: Yes, that blog post does represent just one guy's opinion (hi!) and it does date all the way back to 2014, which is, like, decades in genomics years. :-) By the way, there is quite a bit of literature discussing the improvements that expected read counts derived from an Expectation Maximization algorithm provide over raw read counts. I'd suggest reading the RSEM papers for a start[1][2].
But your main question is about the mechanics of running RSEM and EBSeq. First, RSEM was written explicitly to be compatible with EBSeq, so I'd be very surprised if it does not work correctly out-of-the-box. Second, EBSeq's MedianNorm function worked very well in my experience for normalizing the library counts. Along those lines, the blog you mentioned above has another post that you may find useful.
But all joking aside, these tools are indeed dated. Alignment-free RNA-Seq tools provide orders-of-magnitude improvements in runtime over the older alignment-based alternatives, with comparable accuracy. Sailfish was the first in a growing list of tools that now includes Salmon and Kallisto. When starting a new analysis from scratch (i.e. if you ever get the original FASTQ files), there's really no good reason not to estimate expression using these much faster tools, followed by a differential expression analysis with DESeq2, edgeR, or sleuth.

1Li B, Ruotti V, Stewart RM, Thomson JA, Dewey CN (2010) RNA-Seq gene expression estimation with read mapping uncertainty. Bioinformatics, 26(4):493–500, doi:10.1093/bioinformatics/btp692.
2Li B, Dewey C (2011) RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics, 12:323, doi:10.1186/1471-2105-12-323., Question: I am trying to use samtools depth (v1.4) with the -a option and a bed file listing the human chromosomes chr1-chr22, chrX, chrY, and chrM to print out the coverage at every position:
cat GRCh38.karyo.bed | awk '{print $3}' | datamash sum 1
3088286401

I would like to know how to run samtools depth so that it produces 3,088,286,401 entries when run against a GRCh38 bam file:
samtools depth -b $bedfile -a $inputfile

I tried it for a few bam files that were aligned the same way, and I get differing number of entries:
3087003274
3087005666
3087007158
3087009435
3087009439
3087009621
3087009818
3087010065
3087010408
3087010477
3087010481
3087012115
3087013147
3087013186
3087013500
3087149616

Is there a special flag in samtools depth so that it reports all entries from the bed file?
If samtools depth is not the best tool for this, what would be the equivalent with sambamba depth base?
sambamba depth base --min-coverage=0 --regions $bedfile $inputfile

Any other options?

Answer: You might try using bedtools genomecov instead. If you provide the -d option, it reports the coverage at every position in the BAM file.
bedtools genomecov -d -ibam $inputfile > "${inputfile}.genomecov"

You can also provide a BED file if you just want to calculate in the target  region.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
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Created: 2025-06-04 18:55:00