Gas station without pumps

2019 June 18

Comparing fullgenomes.com with Dante Labs

Filed under: Uncategorized — gasstationwithoutpumps @ 23:58
Tags: , , , , ,

Now that my grading is done, I finally had some time to look at the whole-genome sequencing data I got from fullgenomes.com. I ordered the sequencing on 7 March 2019, shipped the spit kit on 13 March 2019, and got all the data by 3 May 2019.  Their price, $1175, for 30× whole-genome sequencing, is fairly typical of the direct-to-consumer sequencing outfits.  (Dante Labs is much cheaper, but a number of people have been unhappy with the slow or non-delivery of data.)

Here is a summary of what I saw in the fullgenomes snpeff.vep.vcf file:

There are 4906211 genotype sites.
Count by filter values:
	 . 	 4906211
By chromosome, the number of no-call, haploid, and diploid genotypes:
	 chr1 	 0 	 0 	 383857
	 chr2 	 0 	 0 	 390198
	 chr3 	 0 	 0 	 323097
	 chr4 	 0 	 0 	 348597
	 chr5 	 0 	 0 	 286416
	 chr6 	 0 	 0 	 297452
	 chr7 	 0 	 0 	 279633
	 chr8 	 0 	 0 	 240326
	 chr9 	 0 	 0 	 216318
	 chr10 	 0 	 0 	 247472
	 chr11 	 0 	 0 	 233892
	 chr12 	 0 	 0 	 224952
	 chr13 	 0 	 0 	 189629
	 chr14 	 0 	 0 	 150472
	 chr15 	 0 	 0 	 143682
	 chr16 	 0 	 0 	 146914
	 chr17 	 0 	 0 	 136966
	 chr18 	 0 	 0 	 136431
	 chr19 	 0 	 0 	 109775
	 chr20 	 0 	 0 	 125200
	 chr21 	 0 	 0 	 87210
	 chr22 	 0 	 0 	 83531
	 chrX 	 0 	 105204 	 12991
	 chrY 	 0 	 5979 	 0
	 chrM 	 0 	 17 	 0

By type of genotype, there are
	 CT 	 854737
	 AG 	 828712
	 AA 	 416556
	 TT 	 414670
	 CC 	 401031
	 GG 	 400383
	 DI 	 269235
	 AC 	 266527
	 AT 	 245779
	 GT 	 239801
	 CG 	 225762
	 II 	 176278
	 DD 	 46625
	 A 	 26598
	 T 	 26573
	 G 	 23312
	 C 	 23061
	 I 	 11656
	 *T 	 2890
	 *A 	 2522
	 *G 	 1757
	 *C 	 1746

I don’t know what the *T, *A, *G, and *C sites are.  The diploid sites on the X chromosome may be sites that have close homologs on the Y chromosome—close enough that mapping algorithms see them as being the same.

What I wanted to do first was to compare the fullgenomes data with the 23andme genotyping, like I had done with the Dante Labs data.  That turned out to be somewhat difficult, as fullgenomes called variants relative to the latest reference genome (gRCH38), while 23andme and Dante Labs both used the older (gRCH37 =hg19) reference genome.  That difference means that all the coordinates are different, so simple comparisons are difficult.

I have variant calls for the Dante Labs data on both the references using Google’s DeepVariant, so I could compare the Dante Labs calls (which I believe are done with the GATK pipeline) with the DeepVariant calls on the same data, and I could compare the fullgenomes calls with the DeepVariant calls on the Dante Labs data.

I can compare the Dante Labs data with the two variant callers, to see how much difference variant calling makes, and then compare the fullgenomes data with the DeepVariant calls on the Dante Labs data, where both the variant caller and the sequencing method differ.

dantelabs GATK                         |dantelabs-deepvariant
3499617 genotype_sites                 |5884795 genotype_sites                 
  chr   no-call haploid diploid matches|  chr   no-call haploid diploid mismatches
  chrM      0      21       0      0   |  chrM      0       0       0      0   
  chr1      0       0  267787 262884   |  chr1      0       0  446877   4556   
  chr2      0       0  287274 282806   |  chr2      0       0  459298   4145   
  chr3      0       0  240607 237530   |  chr3      0       0  367320   2822   
  chr4      0       0  261070 257783   |  chr4      0       0  400640   2868   
  chr5      0       0  212325 210325   |  chr5      0       0  327319   1827   
  chr6      0       0  204102 201254   |  chr6      0       0  318344   2616   
  chr7      0       0  203647 199384   |  chr7      0       0  350292   3857   
  chr8      0       0  183159 181044   |  chr8      0       0  280030   1902   
  chr9      0       0  148637 143697   |  chr9      0       0  257715   4696   
  chr10     0       0  177729 174246   |  chr10     0       0  295033   3087   
  chr11     0       0  174926 172444   |  chr11     0       0  275753   2256   
  chr12     0       0  165987 163409   |  chr12     0       0  269918   2286   
  chr13     0       0  133928 132587   |  chr13     0       0  199795   1215   
  chr14     0       0  111553 109462   |  chr14     0       0  178071   1979   
  chr15     0       0  103635 101314   |  chr15     0       0  169214   2201   
  chr16     0       0  107064 104448   |  chr16     0       0  194206   2398   
  chr17     0       0   89744  88302   |  chr17     0       0  166728   1300   
  chr18     0       0  100640  99358   |  chr18     0       0  154510   1143   
  chr19     0       0   75073  73488   |  chr19     0       0  146076   1415   
  chr20     0       0   73566  71604   |  chr20     0       0  131282   1844   
  chr21     0       0   52060  50356   |  chr21     0       0  101284   1595   
  chr22     0       0   45153  43796   |  chr22     0       0   84522   1248   
  chrX      0   74099    1801  73134   |  chrX      0   88863   84484   2496   
  chrY      0    1393    2637   1709   |  chrY      0    3098   43760   2020   
  chr1_gl000191_random     0       0       0      0   |  chr1_gl000191_random     0       0      99      0   
  chr1_gl000192_random     0       0       0      0   |  chr1_gl000192_random     0       0     976      0   
  chr4_ctg9_hap1     0       0       0      0   |  chr4_ctg9_hap1     0       0     263      0   
  chr4_gl000193_random     0       0       0      0   |  chr4_gl000193_random     0       0    1887      0   
  chr4_gl000194_random     0       0       0      0   |  chr4_gl000194_random     0       0    2374      0   
  chr6_apd_hap1     0       0       0      0   |  chr6_apd_hap1     0       0      24      0   
  chr6_cox_hap2     0       0       0      0   |  chr6_cox_hap2     0       0     383      0   
  chr6_dbb_hap3     0       0       0      0   |  chr6_dbb_hap3     0       0     284      0   
  chr6_mann_hap4     0       0       0      0   |  chr6_mann_hap4     0       0     798      0   
  chr6_mcf_hap5     0       0       0      0   |  chr6_mcf_hap5     0       0     132      0   
  chr6_qbl_hap6     0       0       0      0   |  chr6_qbl_hap6     0       0     960      0   
  chr6_ssto_hap7     0       0       0      0   |  chr6_ssto_hap7     0       0     812      0   
  chr7_gl000195_random     0       0       0      0   |  chr7_gl000195_random     0       0    3267      0   
  chr8_gl000196_random     0       0       0      0   |  chr8_gl000196_random     0       0      11      0   
  chr8_gl000197_random     0       0       0      0   |  chr8_gl000197_random     0       0       1      0   
  chr9_gl000198_random     0       0       0      0   |  chr9_gl000198_random     0       0    1866      0   
  chr9_gl000199_random     0       0       0      0   |  chr9_gl000199_random     0       0    7465      0   
  chr9_gl000200_random     0       0       0      0   |  chr9_gl000200_random     0       0       1      0   
  chr9_gl000201_random     0       0       0      0   |  chr9_gl000201_random     0       0      12      0   
  chr11_gl000202_random     0       0       0      0   |  chr11_gl000202_random     0       0      78      0   
  chr17_ctg5_hap1     0       0       0      0   |  chr17_ctg5_hap1     0       0     608      0   
  chr17_gl000203_random     0       0       0      0   |  chr17_gl000203_random     0       0     444      0   
  chr17_gl000204_random     0       0       0      0   |  chr17_gl000204_random     0       0      92      0   
  chr17_gl000205_random     0       0       0      0   |  chr17_gl000205_random     0       0    2874      0   
  chr17_gl000206_random     0       0       0      0   |  chr17_gl000206_random     0       0      13      0   
  chr18_gl000207_random     0       0       0      0   |  chr18_gl000207_random     0       0     100      0   
  chr19_gl000208_random     0       0       0      0   |  chr19_gl000208_random     0       0    2216      0   
  chr19_gl000209_random     0       0       0      0   |  chr19_gl000209_random     0       0     371      0   
  chr21_gl000210_random     0       0       0      0   |  chr21_gl000210_random     0       0      21      0   
  chrUn_gl000211     0       0       0      0   |  chrUn_gl000211     0       0    1967      0   
  chrUn_gl000212     0       0       0      0   |  chrUn_gl000212     0       0    1316      0   
  chrUn_gl000213     0       0       0      0   |  chrUn_gl000213     0       0     266      0   
  chrUn_gl000214     0       0       0      0   |  chrUn_gl000214     0       0    2159      0   
  chrUn_gl000215     0       0       0      0   |  chrUn_gl000215     0       0     129      0   
  chrUn_gl000216     0       0       0      0   |  chrUn_gl000216     0       0    8530      0   
  chrUn_gl000217     0       0       0      0   |  chrUn_gl000217     0       0    2138      0   
  chrUn_gl000218     0       0       0      0   |  chrUn_gl000218     0       0    1480      0   
  chrUn_gl000219     0       0       0      0   |  chrUn_gl000219     0       0    5820      0   
  chrUn_gl000220     0       0       0      0   |  chrUn_gl000220     0       0    1730      0   
  chrUn_gl000221     0       0       0      0   |  chrUn_gl000221     0       0     958      0   
  chrUn_gl000222     0       0       0      0   |  chrUn_gl000222     0       0    2193      0   
  chrUn_gl000223     0       0       0      0   |  chrUn_gl000223     0       0      12      0   
  chrUn_gl000224     0       0       0      0   |  chrUn_gl000224     0       0    3738      0   
  chrUn_gl000225     0       0       0      0   |  chrUn_gl000225     0       0   15234      0   
  chrUn_gl000226     0       0       0      0   |  chrUn_gl000226     0       0     257      0   
  chrUn_gl000227     0       0       0      0   |  chrUn_gl000227     0       0      80      0   
  chrUn_gl000228     0       0       0      0   |  chrUn_gl000228     0       0    1299      0   
  chrUn_gl000229     0       0       0      0   |  chrUn_gl000229     0       0    1080      0   
  chrUn_gl000230     0       0       0      0   |  chrUn_gl000230     0       0     409      0   
  chrUn_gl000231     0       0       0      0   |  chrUn_gl000231     0       0    1118      0   
  chrUn_gl000232     0       0       0      0   |  chrUn_gl000232     0       0    2148      0   
  chrUn_gl000233     0       0       0      0   |  chrUn_gl000233     0       0     433      0   
  chrUn_gl000234     0       0       0      0   |  chrUn_gl000234     0       0    2281      0   
  chrUn_gl000235     0       0       0      0   |  chrUn_gl000235     0       0    1224      0   
  chrUn_gl000236     0       0       0      0   |  chrUn_gl000236     0       0     131      0   
  chrUn_gl000237     0       0       0      0   |  chrUn_gl000237     0       0     493      0   
  chrUn_gl000238     0       0       0      0   |  chrUn_gl000238     0       0      19      0   
  chrUn_gl000239     0       0       0      0   |  chrUn_gl000239     0       0      80      0   
  chrUn_gl000240     0       0       0      0   |  chrUn_gl000240     0       0     696      0   
  chrUn_gl000241     0       0       0      0   |  chrUn_gl000241     0       0    1665      0   
  chrUn_gl000242     0       0       0      0   |  chrUn_gl000242     0       0      32      0   
  chrUn_gl000243     0       0       0      0   |  chrUn_gl000243     0       0     328      0   
  chrUn_gl000244     0       0       0      0   |  chrUn_gl000244     0       0      83      0   
  chrUn_gl000245     0       0       0      0   |  chrUn_gl000245     0       0     110      0   
  chrUn_gl000246     0       0       0      0   |  chrUn_gl000246     0       0      73      0   
  chrUn_gl000247     0       0       0      0   |  chrUn_gl000247     0       0     196      0   
  chrUn_gl000248     0       0       0      0   |  chrUn_gl000248     0       0      26      0   

  total     0   75513 3424104 3436364   |  total     0   91961 5792834  57772   

Count of types of genotype
   CT  696904                          |   CT  777273                          
   AG  696669                          |   AG  757560                          
   CC  358678                          |   CC  686562                          
   AA  319335                          |   AA  715299                          
   TT  320081                          |   TT  713424                          
   GG  358891                          |   GG  633290                          
   AC  172678                          |   AC  228469                          
   GT  173342                          |   GT  208315                          
   CG  178718                          |   CG  196041                          
   AT  148808                          |   AT  212648                          
   DD       0                          |   DD  249895                          
   DI       0                          |   DI  237611                          
   II       0                          |   II  176447                          
   T    18873                          |   T    22689                          
   A    18752                          |   A    22586                          
   G    19064                          |   G    20696                          
   C    18824                          |   C    20492                          
   I        0                          |   I     5271                          
   D        0                          |   D      227                          

DeepVariant makes a lot more calls (mainly because it also reports places where it decides that the genotype is homozygous reference, which GATK doesn’t report), but also because the GATK calls were filtered to remove the low-evidence calls, while DeepVariant was set up to report everything.
DeepVariant does a huge number of diploid calls on X and Y, which is a little suspicious.
The ratio of mismatches to matches is 0.01681, about a 1.65% discrepancy rate. I don’t know which of the genome callers is better on this data, but DeepVariant was supposedly better on some recent tests on autosomal chromosomes (I’ve not looked up the paper yet).

Comparing the Dante Labs DeepVariant calls with the fullgenomes calls (on gRCH38) shows a bigger difference:


dantelabs-deepvariant                  |fullgenomes snpeff.vep.vcf.gz
5508932 genotype_sites                 |4906211 genotype_sites                 
  chr   no-call haploid diploid matches|  chr   no-call haploid diploid mismatches
  chr1      0       0  443442 336398   |  chr1      0       0  383857  23983   
  chr2      0       0  434888 356031   |  chr2      0       0  390198  17928   
  chr3      0       0  355613 296731   |  chr3      0       0  323097  12684   
  chr4      0       0  380259 320989   |  chr4      0       0  348597  13450   
  chr5      0       0  316748 262579   |  chr5      0       0  286416  11314   
  chr6      0       0  299986 253300   |  chr6      0       0  297452   8599   
  chr7      0       0  314759 252073   |  chr7      0       0  279633  14099   
  chr8      0       0  259602 217907   |  chr8      0       0  240326   8761   
  chr9      0       0  242579 183556   |  chr9      0       0  216318  19382   
  chr10     0       0  280582 224653   |  chr10     0       0  247472  12447   
  chr11     0       0  256993 214312   |  chr11     0       0  233892   8491   
  chr12     0       0  252220 205678   |  chr12     0       0  224952   8601   
  chr13     0       0  209801 165921   |  chr13     0       0  189629  11023   
  chr14     0       0  161466 132483   |  chr14     0       0  150472   6803   
  chr15     0       0  154740 123209   |  chr15     0       0  143682   8381   
  chr16     0       0  169234 128717   |  chr16     0       0  146914   9197   
  chr17     0       0  158498 111002   |  chr17     0       0  136966  12128   
  chr18     0       0  150941 123100   |  chr18     0       0  136431   5881   
  chr19     0       0  129958  94953   |  chr19     0       0  109775   6398   
  chr20     0       0  147206  95398   |  chr20     0       0  125200  20597   
  chr21     0       0   94029  64558   |  chr21     0       0   87210  14591   
  chr22     0       0   93982  58824   |  chr22     0       0   83531  14887   
  chrX      0   93759   70714  96500   |  chrX      0  105204   12991  14629   
  chrY      0    2818   34115   2134   |  chrY      0    5979       0   3321   
  chrM      0       0       0      0   |  chrM      0      17       0      0   

  total     0   96577 5412355 4321006   |  total     0  111200 4795011 287575   

Count of types of genotype
   CT  761277                          |   CT  854737                          
   AG  741910                          |   AG  828712                          
   AA  636195                          |   AA  416556                          
   TT  635850                          |   TT  414670                          
   CC  609922                          |   CC  401031                          
   GG  557555                          |   GG  400383                          
   DI  234030                          |   DI  269235                          
   AC  224266                          |   AC  266527                          
   AT  209513                          |   AT  245779                          
   GT  204389                          |   GT  239801                          
   CG  192211                          |   CG  225762                          
   II  173913                          |   II  176278                          
   DD  231324                          |   DD   46625                          
   A    23799                          |   A    26598                          
   T    23687                          |   T    26573                          
   G    21821                          |   G    23312                          
   C    21561                          |   C    23061                          
   I     5438                          |   I    11656                          
   *T       0                          |   *T    2890                          
   *A       0                          |   *A    2522                          
   *G       0                          |   *G    1757                          
   *C       0                          |   *C    1746                          
   D      271                          |   D        0                          

Now the ratio of mismatches to matches is 0.06655, a 6.2% discrepancy rate. A few of the discrepancies were haploid/diploid differences on chromosome X, but that is only about 1100 differences. None of the mismatches involve the weird *A,*C,*G, *T genotype calls.

I do have 4,321,006 genotype calls that I am now pretty confident of, as they were called by two different variant callers from two different sequencing runs using different sequencing technology.

But I’m not sure which data set or variant caller to favor on the 287,575 disagreements, nor what to do about the locations where one variant caller made a call for a site and the other didn’t. The fullgenomes data includes a gVCF file, which has calls for every base that got reads mapped to it, but I’ve not tried extracting data from that format yet (it’s bad enough having to try to extract data from the two different vcf formats).

I was planning to compare the 23andme data with each of the whole-genome vcf calls, making the assumption that the sequencing and variant caller that agrees most with the hybridization-based genotyping by 23andme would be the most accurate. (I also want to make a revised “23andme” data set that replaces any genotyping calls where both whole-genome sequences agreed with each other, but disagreed with 23andme.)

To make this all work, I need to have all the variant calling be relative to the same reference genome, which means either lifting Dante Labs and 23andme to gRCH38 or reversing that and moving the fullgenomes vcf files to gRCH37. I could also try having Kishwar do the DeepVariant calls on both reference genomes for the fullgenomes data.

I’ll need to think a bit about what would be most useful.

2019 February 17

Full-genome sequencing pricing

Filed under: Uncategorized — gasstationwithoutpumps @ 12:23
Tags: , ,

In the comments on Dante Labs is a scam, there has been some discussion on pricing of whole-genome sequencing.  There are a lot of companies out there with different business models, different pricing schemes, and subtly different offerings—all of which is undoubtedly confusing to consumers.  I’ve been trying to collect pricing information for the past year, and I’m still often confused by the offerings.

Consumers buy sequencing for two main purposes: to find out about their ancestry and to find out about the genetic risks to their health.

For ancestry, there is no real need for sequencing—the information from DNA microarrays (as used by companies like 23andme or ancestry.com) is more than sufficient, and those companies have big proprietary databases that allow more precise ancestry information than the public databases accessible to companies that do full sequencing.  The microarray approach is currently far cheaper than sequencing, though the difference is shrinking.

The major, well-documented risk factors for health are also covered by the DNA microarrays, but there are thousands of risk factors being discovered and published every year, and the DNA microarray tests need to redesigned and rerun on a regular basis to keep up. If whole-genome sequencing is done, almost all of the data needed for analysis is collected at once, and only analysis needs to be redone.  (This is not quite true—long-read sequencing is beginning to provide information about structural rearrangements of the genome that are not visible in the older short-read technologies, and some of these structural rearrangements are clinically significant, though usually only in cancer tumors, not in the germ line.)

For most consumers mildly interested in ancestry and genetic risks, the 23andme $200 package is all they need.  If they are just interested in ancestry, there are even cheaper options ($100 from 23andme or ancestry.com—I have no idea which is better).

My interest in my genome is to try to figure out the genetics of my inherited low heart rate.  It is not a common condition, and it seems to be beneficial rather than harmful (at any rate, my ancestors who had it were mostly long-lived), so the microarrays are not looking for variants that might be responsible.  Whole genome sequencing would give me a much larger pool of variants to examine to try to track down the cause.  To get high probability of seeing every variant, I would need 30× sequencing of my whole genome.  If I thought that the problem was in a protein-coding gene, I could get 100× exome sequencing instead.

The problem with whole-genome sequencing is that everybody has about a million variants, almost all of which are irrelevant to any specific health question.  The variants that have already been studied and well documented are not too hard to deal with, but most of them are already in the DNA microarrays, so whole-genome sequencing doesn’t offer much more on them.  Looking for a rare variant that has not been well studied is much harder—which of the millions of base changes matters?

The popular, and expensive, approach in recent genomics literature is to do genome-wide association studies (GWAS).  These take a large population of people with and without the phenotype of interest, then looks for variants that reliably separate the groups.  If there are many possible hypotheses (generally in the thousands or millions), a huge population is needed to separate out the real signal from random noise.  Many of the early GWAS papers were later shown to have bogus results, because the researchers did not have a proper appreciation of how easy it was to fool themselves.

Earlier studies focussed on families, where there is a lot of common genetic background, and each additional person in the study cuts the candidate hypothesis pool almost in half.  To narrow down from a million candidate variants to only one would take a little over 20 closely related people (assuming that the phenotype was caused by just a single variant—always a dangerous assumption).  I can probably get 4 or 5 of my relatives to participate in a study like this, but probably not 20.  I don’t think I want to pay for 20 whole-genome sequencing runs out of my own pocket anyway.

I have some hope of working with a smaller number of samples, though, as there has been an open-access paper on inherited bradycardia implicating about 16 genes.  If I have variants in those genes or their promoters, they are likely to be the interesting variants, even if no one has previously seen or studied the variants.  Of course, the size of the region means I’m likely to have about 80 variants in those regions just by chance, so I’ll still need to have some of my relatives’ genomes to narrow down the possibilities, but 8 or 9 relatives may be enough to get a solid conjecture.  (Proving that the variant is responsible would be more difficult—I’d either need a much larger cohort or someone would have to do genetic experiments in animal models.)

How expensive is the whole-genome sequencing anyway?  It can be hard to tell, as different labs offer different packages and many require more than the advertised price.

A university research lab like UC Davis will do the DNA library prep and 30× sequencing for about $1000, but not the extraction of the DNA from a spit kit or cheek swabs.  That is a fairly cheap procedure (about $50, I think), but arranging for one lab to do the extraction and ship to another lab increased the complexity of the logistics, to the point where I don’t think I’d ever get around to doing it.  Storing the sequencing results (FASTQ files), doing the mapping of the reads to a reference genome to get BAM files, and calling variants to get VCF files adds to the cost, though cloud-based systems are available that make this reasonably cheap (I think about $50 a year for storage and about $50 for the analysis).  Interpreting the VCF files can be aided by using Promethease for $12 to find relevant entries in SNPedia.

Fullgenomes.com offers packages from $545 to $2900, with an extra $250 for analysis.  The most relevant package for what I want would be the 30× sequencing package for $1295, probably without their $250 analysis, which I suspect is not much more than consumer-friendly rewrite of the results from Promethease (which can be very hard to read, so most consumers would need the rewrite).  Their pricing is a little weird, as the 15× sequencing is less than half the price of 30×, while the underlying technology should make the 30× cheaper per base.  I’ll have to check on exactly what is included in the $1295 package, as that is looking like the best deal I can find right now.

BGI advertises bulk whole-genome sequencing at low prices for researchers, but never responded to my email (from my university account) trying to get actual prices.  A lot of other companies (like Novogene) also have “request a quote” buttons.  My usual reaction to that is that if you have to ask the price, you can’t afford it.  Secret pricing is almost always ridiculously high pricing, and I prefer not to deal with companies that have secret pricing.

Dante Labs advertises very low prices, but does not deliver results—they seem to be a scam.

Veritas Genetics offers a low price ($999), but that does not include giving you back your data—they want to hang onto it and sell you additional “tests” that cost ridiculously large amounts.  I believe they will sell the VCF file (but not the BAM or FASTQ files it is based on) for an additional fee.

Most of the other companies I’ve seen have 30× whole-genome sequencing priced at over $2000, which is a little out of my price range.

 

2014 September 30

Ebola genome browser

Filed under: Uncategorized — gasstationwithoutpumps @ 21:00
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For the past week, I’ve been watching the genome browser team (led by Jim Kent) scramble to get together an information resource to aid in the fight against the Ebola virus.  They went public today:

We are excited to announce the release of a Genome Browser and information portal for the Jun. 2014 assembly of the Ebola virus (UCSC version eboVir3, GenBank accession KM034562) submitted by the Broad Institute. We have worked closely with the Pardis Sabeti lab at the Broad Institute and other Ebola experts throughout the world to incorporate annotations that will be useful to those studying Ebola. Annotation tracks included in this initial release include genes from NCBI, B- and T-cell epitopes from the IEDB, structural annotations from UniProt and a wealth of SNP data from the 2014 publication by the Sabeti lab. This initial release also contains a 160-way alignment comprising 158 Ebola virus sequences from various African outbreaks and 2 Marburg virus sequences. You can find links to the Ebola virus Genome Browser and more information on the Ebola virus itself on our Ebola Portal page.

Bulk downloads of the sequence and annotation data are available via the Genome Browser FTP server or the Downloads page. The Ebola virus (eboVir3) browser annotation tracks were generated by UCSC and collaborators worldwide. See the Credits page for a detailed list of the organizations and individuals who contributed to this release and the conditions for use of these data.


Matthew Speir
UCSC Genome Bioinformatics Group

2012 June 21

Crowdfunding genome project

Filed under: Uncategorized — gasstationwithoutpumps @ 20:37
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Manuel Corpas is trying to get the genome of 5 members of his family sequenced, so that he can release the data for public analysis and development of genome analysis tools.

Crowdfunding Genome Project] Day 2: BGI Officially Agrees Sequencing « Manuel Corpas’ Blog.

Donations Sought For Whole Genome Sequencing: 40 Days To Go!

He previously released the genotyping of the same 5 members of his family, so you know that he is serious about doing a public release of the data.

2011 June 20

Human mutation rates

Filed under: Uncategorized — gasstationwithoutpumps @ 10:02
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I just finished reading an article on human mutation rates:

Variation in genome-wide mutation rates within and between human families by Donald F Conrad, Jonathan E M Keebler, Mark A DePristo, Sarah J Lindsay, Yujun Zhang, Ferran Casals, Youssef Idaghdour, Chris L Hartl, Carlos Torroja, Kiran V Garimella, Martine Zilversmit, Reed Cartwright, Guy A RouleauMark Daly, Eric A Stone, Matthew E Hurles,& Philip Awadalla for the 1000 Genomes Project
Nature Genetics
(2011) Published online 12 June 2011
doi:10.1038/ng.862

The article computes mutation rates for two triples (father, mother, and child) who have been thoroughly re-sequenced as part of the 1000 genomes project.  For each triple, they identify possible sites of de novo mutations (appearing in the child but not inherited from either parent) using different methods, then re-examine each of the possible candidates with further sequencing, to try to separate out germ-line (inheritable) mutations from somatic (in the body) or cell-culture mutations.

They found that few of the observed de novo mutations in the sequencing were actually germ-line mutations (only about one in 20).  The final mutation rates they get were about 1e-8 (one change in 108 bases).  This rate is comparable with sex-averaged rates from other more population-based estimates, but at the low end.  They point out that mutation rates may vary between individuals (based on age and environmental conditions), and that a few high-mutation-rate individuals may  make the mean rate over many generations higher than the most frequently observed rate at the current time, so both the 1e-8 rate and the highest estimates (4e-8 for paternal mutations estimated from species-divergence from chimps) may still be consistent.  Other possible explanations for the wide spread are given—for example, that the divergence from chimp may be further back in time than the current best estimates.

If we take the 1e-8 error rate as typical, we would expect to see about 60 de novo mutations in each individual (remember that the 3Gbase human genome size is the haploid size, but humans are diploid, so we inherit about 6Gbases from our parents).  The variation from person to person could be quite wide though, even if there were no environmental factors affecting the mutation rate—a Poisson process has a standard deviation of the square root of the mean, so  mean 60 implies a standard deviation of about 8.

One surprising result they got was that for one of the triples, the paternal mutation rate was lower than the maternal one (most estimates have the paternal mutation rate around 4 times the maternal rate, attributed to higher numbers of replications of DNA in the male germ line).  The ages of the parents at conception was not recorded for either triple, but age almost certainly plays a major role in mutation rate. The 4 estimates of mutation rate they got (2 maternal and 2 paternal) had about an 8-to-1 range (much wider than the error bars on the individual estimates), so clearly many more triples need to be examined to get a broader picture of maternal and paternal mutation rates in the population as a whole.  It would be good to have triples in which the ages of the parents are recorded, and to have further generations sequenced to make germline/non-germline mutations easier to separate.

Estimated mutation rates, with previously published estimates above the green line, and new ones below it. Figure copied from the article.

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