Just like the title says, I can't find any definitive info saying 100%
of all the basepairs in a human genome have been sequenced. What does
this mean? When will be able to sequence the DNA wound up in the
heterochromatin?
http://www.yanaiweb.com/genome/1.%20Evolution/Lander_Nature_2011.pdf
and this also wasn't too helpful:
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Science 9 October 2009:
Vol. 326 no. 5950 pp. 236-237
DOI: 10.1126/science.1180614
Policy Forum
Genomics
Genome Project Standards in a New Era of Sequencing
P. S. G. Chain1,2,3,*†§, D. V. Grafham4,†§, R. S. Fulton5,†,
M. G. FitzGerald6,†, J. Hostetler7,†, D. Muzny8,†, J. Ali9,
B. Birren6, D. C. Bruce1,10,
C. Buhay8, J. R. Cole3, Y. Ding8, S. Dugan8, D.
Field11, G. M. Garrity3, R. Gibbs8, T. Graves5, C. S.
Han1,10, S. H. Harrison3,*, S. Highlander8,
P. Hugenholtz1, H. M. Khouri12, C. D. Kodira6,*, E.
Kolker13,14, N. C. Kyrpides1, D. Lang12, A. Lapidus1, S.
A. Malfatti12, V. Markowitz15,
T. Metha6, K. E. Nelson7, J. Parkhill4, S. Pitluck1,
X. Qin8, T. D. Read16, J. Schmutz17, S. Sozhamannan18, P.
Sterk11, R. L. Strausberg7,
G. Sutton7, N. R. Thomson4, J. M. Tiedje3, G. Weinstock5,
A. Wollam5,
Genomic Standards Consortium Human Microbiome Project Jumpstart
Consortium‡ and
J. C. Detter10,†‡
+ Author Affiliations
1U.S. Department of Energy Joint Genome Institute.
2Lawrence Livermore National Laboratory.
3Michigan State University.
4The Sanger Institute.
5Washington University School of Medicine.
6The Broad Institute.
7J. Craig Venter Institute.
8Baylor College of Medicine.
9Ontario Institute for Cancer Research.
10Los Alamos National Laboratory.
11Natural Environmental Research Council Centre for Ecology and Hydrology.
12National Center for Biotechnology Information.
13Seattle Children's Hospital and Research Institute.
14University of Washington School of Medicine.
15Lawrence Berkeley National Laboratory.
16Emory GRA (Georgia Research Alliance) Genomics Center.
17HudsonAlpha Institute.
18Naval Medical Research Center.
*Full affiliations are available on Science Online.
†Finishing in the Future Working Group members.
‡These authors contributed equally to organizing this work.
§Authors for correspondence. E-mail: pchain@lanl.gov (P.S.G.C.);
dg1@sanger.ac.uk (D.V.G.)
For over a decade, genome sequences have adhered to only two standards
that are relied on for purposes of sequence analysis by interested
third parties (1, 2). However, ongoing developments in revolutionary
sequencing technologies have resulted in a redefinition of traditional
whole-genome sequencing that requires reevaluation of such standards.
With commercially available 454 pyrosequencing (followed by Illumina,
SOLiD, and now Helicos), there has been an explosion of genomes
sequenced under the moniker "draft"; however, these can be very poor
quality genomes (due to inherent errors in the sequencing
technologies, and the inability of assembly programs to fully address
these errors). Further, one can only infer that such draft genomes may
be of poor quality by navigating through the databases to find the
number and type of reads deposited in sequence trace repositories (and
not all genomes have this available), or to identify the number of
contigs or genome fragments deposited to the database. The difficulty
in assessing the quality of such deposited genomes has created some
havoc for genome analysis pipelines and has contributed to many wasted
hours. Exponential leaps in raw sequencing capability and greatly
reduced prices have further skewed the time- and cost-ratios of draft
data generation versus the painstaking process of improving and
finishing a genome. The result is an ever-widening gap between drafted
and finished genomes that only promises to continue (see the figure,
page 236); hence, there is an urgent need to distinguish good from
poor data sets.
The sequencing institutes and consortia whom we represent believe that
a new set of standards is required for genome sequences. The following
represents community-defined categories of standards that better
reflect the quality of the genome sequence, based on our understanding
of the technologies, available assemblers, and efforts to improve upon
drafted genomes. Due to the increasingly rapid pace of genomics, we
avoided rigid numerical thresholds in our definitions to take into
account products achieved by any combination of technology, chemistry,
assembler, or improvement and/or finishing process.
Standard Draft: minimally or unfiltered data, from any number of
different sequencing platforms, that are assembled into contigs. This
is the minimum standard for a submission to the public databases.
Sequence of this quality will likely harbor many regions of poor
quality and can be relatively incomplete. It may not always be
possible to remove contaminating sequence data. Despite its
shortcomings, Standard Draft is the least expensive to produce and
still possesses useful information.
High-Quality Draft: overall coverage representing at least 90% of the
genome or target region. Efforts should be made to exclude
contaminating sequences. This is still a draft assembly with little or
no manual review of the product. Sequence errors and misassemblies are
possible, with no implied order and orientation to contigs. This is
appropriate for general assessment of gene content.
Improved High-Quality Draft: additional work has been performed beyond
the initial shotgun sequencing and High-Quality Draft assembly, by
using either manual or automated methods. This should contain no
discernable misassemblies and should have undergone some form of gap
resolution to reduce the number of contigs and supercontigs (or
scaffolds). Undetectable misassemblies are still possible,
particularly in repetitive regions. Low-quality regions and potential
base errors may also be present. This standard is normally adequate
for comparison with other genomes.
Annotation-Directed Improvement: may overlap with the previous
standards, but the term emphasizes the verification and correction of
anomalies within coding regions, such as frameshifts, and stop codons.
It will most often be used in cases involving complex genomes where
improvement beyond this category fails to outweigh the associated
costs. Gene models (gene calls, including intronexon determination for
eukaryotes) and annotation of the genomic content should fully support
the biology of the organism and the scientific questions being
investigated. Exceptions to this gene-specific finishing standard
should be noted in the submission. Repeat regions at this level are
not resolved, so errors in those regions are much more likely. This
standard is useful for gene comparisons, alternative splicing
analysis, and pathway reconstruction.
Noncontiguous Finished: describes high-quality assemblies that have
been subject to automated and manual improvement, and where closure
approaches have been successful for almost all gaps, misassemblies,
and low-quality regions. Attempts have been made to resolve all gap
and sequence uncertainties, and only those recalcitrant to resolution
remain (with notations in the genome submission as to the nature of
the uncertainty). This product is thus of "Finished" quality with the
only exception being repetitive or intractable gaps, along with
heterochromatic sequence for eukaryotic applications. Thus, it is
appropriate for most analyses. For nearly all higher organisms, this
is the grade previously called "Finished."
Finished: refers to the current gold standard; genome sequences with
less than 1 error per 100,000 base pairs and where each replicon is
assembled into a single contiguous sequence with a minimal number of
possible exceptions commented in the submission record. All sequences
are complete and have been reviewed and edited, all known
misassemblies have been resolved, and repetitive sequences have been
ordered and correctly assembled. Remaining exceptions to highly
accurate sequence within the euchromatin are commented in the
submission. The Finished product is appropriate for all types of
detailed analyses and acts as a high-quality reference genome for
comparative purposes. Some microbial genome sequences where multiple
platforms have been used for the same genome have exceeded this
standard, and it is believed that no bases are incorrect except for
natural, low-level biological variation.
Intermediate standards often overlap, and although we do not advocate
any one standard, we recommend that the target standard be based on
the needs and goals of each project. There may be cases where select
regions will be targeted for improvement and more than one standard
may apply (such regionally improved sequences should be identified).
This approach is most often used for eukaryotic whole-genome
sequencing projects, where the cost of complete finishing remains
prohibitive, and allows improvement to be directed at euchromatic
sequence, because heterochromatic sequence remains largely
recalcitrant to available approaches. Legacy eukaryotic tiling path
standards will remain in use for a time.
Here, we have attempted to capture in a technology-independent fashion
the types of whole-genome sequencing projects that are beginning to
populate databases, and we have defined a set of standards that
accommodate a growing list of alternative genome products that have
been obtained via less conventional means, such as environmental
(metagenomic) or single-cell sequencing. Ongoing discussions with
genome database repositories have been met with enthusiasm, and the
implementation of these standards as a requirement for genome
submissions is expected. To aid in adoption of this classification of
sequence finishing standards, we have added this classification to the
Sequence Ontology (3) where it can now be used to comply with the
Genomic Standards Consortium's (GSC) "Minimum Information about a
Genome Sequence" standard (4) "sequencing status" descriptor.
Furthermore, the efforts described here recently have been adopted
under the umbrella of the GSC (5). This common currency in defining
the products of genome projects enables better management of
expectations and allows users of genomic data to assess the quality of
the deposited available sequences and decide whether these meet their
needs.
--
Nathan McCorkle
Rochester Institute of Technology
College of Science, Biotechnology/Bioinformatics
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