1000 Plant Genomes Project

1000 Plant Genomes Project
Funding agency	Alberta Innovates Technology Futures Alberta Agricultural Research Institute (AARI) Genome Alberta University of Alberta BGI China National GeneBank (CNGB) Musea Ventures (Somekh Family Foundation)
Duration	2008 – 2019
Website	www.onekp.com

The 1000 Plant Transcriptomes Initiative (1KP) was an international research effort to establish the most detailed catalogue of genetic variation in plants. It was announced in 2008 and headed by Gane Ka-Shu Wong and Michael Deyholos of the University of Alberta. The project successfully sequenced the transcriptomes (expressed genes) of 1000 different plant species by 2014;^[1]^[2] its final capstone products were published in 2019.^[3]^[4]^[5]

1KP was one of the large-scale (involving many organisms) sequencing projects designed to take advantage of the wider availability of high-throughput ("next-generation") DNA sequencing technologies. The similar 1000 Genomes Project, for example, obtained high-coverage genome sequences of 1000 individual people between 2008 and 2015, to better understand human genetic variation.^[6]^[7] This project providing a template for further planetary-scale genome projects including the 10KP Project sequencing the whole genomes of 10,000 Plants,^[8] and the Earth BioGenome Project, aiming to sequence, catalog, and characterize the genomes of all of Earth's eukaryotic biodiversity.^[9]

YouTube Encyclopedic

1/5
Views:
25 971
750
768
1 158
1 366

Transcription

[music playing] Lisa Brooks: 1000 Genomes Project is looking at the genomes at 2,500 people to find human genetic variation. This is places in the DNA where people differ from each other, and the goal of this is - - is the data from this project will be used to find genes that affect disease. Steven Sherry: I frequently think of 1000 Genomes as a Lewis and Clark expedition to the interior of the continent. We knew the outlines and the contours. We understood where the major rivers and the bays were, but what we�re doing now is kind of systematically kind of walking though the interior into uncharted territory of rare variance. Gilean McVean: The idea behind a 1000 Genomes project is to build a map of human genetic variation, down to frequencies of about a percent in most of the populations around the world. Lisa Brooks: Some genetic variation is very common. Things like ADO blood type, the variants that contribute to that are in every population. If you look at any one population or any population, you�ll find those genes. But some variants are much rarer or rare in families and specific populations, so in order to get a good picture of human genetic variation, you have to look at a lot of people. David Altshuler: So the project actually oversaw the collection of what is now a collection of 2,500 individuals from 26 different population groups around the world, all with informed consent that allows this open sharing of data. And then the project has put together these sequencing methods and technologies with these DNA samples and is producing an unmatched catalogue, at least to date, of genetic variation in human beings around the world. Steven Sherry: We are building that catalogue of what normal looks like and, and so it�s available for a lot of disease studies to use immediately. [music playing] Lisa Brooks: The main findings were, first off, the amount of variation. There�s 40 million variants in that paper, which is a very large number of variants across these 1,092 people. Gilean McVean: The things I found most surprising were to do with the number of apparently bad mutations that we all carry. So there are these catalogues out there, which try to list mutations that have been though clinical labs is being identified as pathogenic. We�re all walking around with between two and five of these mutations, but most of us are apparently healthy. Lisa Brooks: The other thing that was found -- and this is not unexpected, but this actually gave the details of that -- is common variants, variants that are frequent in any one population are frequent in all populations. Again, this reflects our common, shared ancestry from Africa. Gilean McVean: I think another really important finding is that these rarer variants -- and these are the ones that, you know, we might think of as being more likely to cause more severe diseases -- are very structured geographically, which means that the rare variants that you find in the U.K. are different from what you find in Italy or Germany or the U.S. So there�s quite strong geographical restriction of very low frequency, possibly quite bad for you mutations, so that when you�re looking at an individual who�s got the disease, you really need to take into context their local genetic background, the geographic ancestry essentially of these individuals. Lisa Brooks: And that has real implications for how you design disease studies, because a lot of people want to say -- to be efficient with money, and of course NIH appreciates that; a lot of people want to use a shared control group, and then look at their disease study, their autism, the Type 2 diabetes, arthritis against a common disease group. But it turns out, because of this differentiation in rare variants, and because a lot of people nowadays are interested in understanding how rare variants contribute to disease, you can�t take one population and study diabetes in it, and a different population and use it as your control group, because those rare variants will result in a lot of false positives. You�ll get wrong results if you do that. So that means you have to be very careful how you design your studies so that you match your control group very carefully with your case group. [music playing] Gilean McVean: But actually genetic variation in its own, on its own, doesn�t make a huge amount of sense; it�s not wildly interesting. It only makes sense in the light of the phenotypes that we can collect. What people look like, what diseases they�re going to get, how they respond to different drugs. Lisa Brooks: So there�s a couple ways in which 1000 Genomes data are being used. David Altshuler: The data can be used to support studies of the genetic variance found in the 1000 Genomes Project for their role in disease. Lisa Brooks: So you look through the whole genome, you look for regions that differ in frequency between people with the disease, people without disease, and those are regions that you want to follow up on, because they�re quite likely to contain genes that will contribute to the disease risk. David Altshuler: In many of our diabetes studies, we�ve identified common genetic variants that are associated with risk of Type 2 Diabetes. There are actually now over 50 such regions of the human genome that have been identified as contributing somehow to diabetes risk, but in very few of these do we actually know the causal mutation in that gene region that is actually why or how that gene region contributes to disease. And because before the 1000 Genomes Project and other sequencing studies, we only had a partial list of the genetic variance in each of those regions, it was like trying to find the needle in a haystack without having the ability to see all the pieces of hay and the needles. Lisa Brooks: So what you can do is, instead of having to sequence all these people, you can look in the database and look at a 1000 Genomes� data and say, �Okay, in these regions, this -- these are all the variants.� Almost all, as I say; it�s not going to find every last one, but it�s almost all the variation of that region, which means each study doesn�t have to sequence its own people. It can just go to the computer and find most of the variation that�s there. Gilean McVean: Got a colleague here who�s interested in multiple sclerosis. He picked up on a gene that called -- had been pulled out of genome-wide associations study. He looked in the 1000 Genomes data and found that there was a variant that seemed to be explaining most of the signal. [music playing] Steven Sherry: The 1000 Genomes data set for phase one is on the order of a 180 terabytes. So that�s 180,000 gigabytes. Remember a DVD is four gig, right? So take 180,000 and divide it by four, and that�s how many DVD�s worth of data we have. It is by far the largest public bioinformatics dataset in the world. David Altshuler: A number of different sequencing technology companies have been active participants in the project, and many different sequencing laboratories around the world have employed these different methods. And together the project has worked to make it so that it�s much more practical than it was to take data with one machine, or one lab, and make it compatible and comparable to data generated at a different time with a different machine in a different lab. That might sound very easy, but it turns out to be extremely difficult. Lisa Brooks: Because once you take the raw sequence reads, you have to map them on to the genome, then have to call variance. Point places in the DNA where people differ called SNPs, single nucleotide polymorphisms, are more or less easy to make calls on. Structural variants where there�s an insertion, or a deletion, or a piece of a DNA�s slipped over, or a piece of DNA has moved over there, or it�s been copied, the copy number variance, those are actually much harder to deal with, and so there�s a very active part of the analysis group talking about structural variance. Gilean McVean: I think the 1000 Genomes Project has been the driving project for working out how to sequencing genome. Lisa Brooks: So an interesting thing about this project has been, there�s been huge numbers of analysts from a lot of institutions. Some of them have come from the sequencing centers, but many of them come from various other places in the U.S., in Europe and China to work on these data. And so the group has an analysis group call every week, and so many, many people have been working very cooperatively to produce the dataset. Steven Sherry: Just within this last year there have been over 5,000 institutions around the world, over 2,000 in the United States, and 3,000 everywhere else that have downloaded parts of 1000 -- at least some part of 1000 Genomes. There are research universities and teaching hospitals that maintain copies of everything. [music playing] David Altshuler: I think the legacy of these projects are numerous. One is that we will have succeeded, I think, in making sure that our shared genetic heritage, and our ancestry, and the DNA variants we carry are freely available for researchers to use to benefit patients around the world. There was a time 10 years ago -- and there�s always this tendency -- where some said maybe we should patent all these and lock them up so we can individually profit from that, but we tend to think that these genetic variants belong to all of us. They�re not -- they�re not inventions, they�re simply things we carry, and they should be available. Gilean McVean: I think when historians look back on the 1000 Genomes Project, they�ll see it primarily as a driver for turning the idea of whole genome sequencing for personal medical use into reality. It was the key defining moment in making that happen, because it�s been a catalyst for getting the technology together, getting the resource together that can tell us what genetic variants are out there, so that we can interpret those found in specific individuals. [music playing] [end of transcript] NIH/NHGRI: 1000 Genomes Final Stereo 1 06/05/14 Prepared by National Capitol Captioning 200 N. Glebe Rd. #1016 (703) 243-9696 Arlington, VA 22203

Goals

As of 2002^[update], the number of classified green plant species was estimated to be around 370,000, however, there are probably many thousands more yet unclassified.^[10] Despite this number, very few of these species have detailed DNA sequence information to date; 125,426 species in GenBank, as of 11 April 2012^[update],^[11] but most (>95%) having DNA sequence for only one or two genes. "...almost none of the roughly half million plant species known to humanity has been touched by genomics at any level".^[1] The 1000 Plant Genomes Project aimed to produce a roughly a 100x increase in the number of plant species with available broad genome sequence.

Evolutionary relationships

There have been efforts to determine the evolutionary relationships between the known plant species,^[12]^[13] but phylogenies (or phylogenetic trees) created solely using morphological data, cellular structures, single enzymes, or on only a few sequences (like rRNA) can be prone to error;^[14] morphological features are especially vulnerable when two species look physically similar though they are not closely related (as a result of convergent evolution for example) or homology, or when two species closely related look very different because, for example, they are able to change in response to their environment very well. These situations are very common in the plant kingdom. An alternative method for constructing evolutionary relationships is through changes in DNA sequence of many genes between the different species which is often more robust to problems of similar-appearing species.^[14] With the amount of genomic sequence produced by this project, many predicted evolutionary relationships could be better tested by sequence alignment to improve their certainty. With 383,679 nuclear gene family phylogenies and 2,306 gene age distributions with Ks plots used in the final analysis and shared in GigaDB alongside the capstone paper.^[15]

Biotechnology applications

The list of plant genomes sequenced in the project was not random; instead plants that produce valuable chemicals or other products (secondary metabolites in many cases) were focused on in the hopes that characterizing the involved genes will allow the underlying biosynthetic processes to be used or modified.^[1] For example, there are many plants known to produce oils (like olives) and some of the oils from certain plants bear a strong chemical resemblance to petroleum products like the Oil palm and hydrocarbon-producing species.^[16] If these plant mechanisms could be used to produce mass quantities of industrially useful oil, or modified such that they do, then they would be of great value. Here, knowing the sequence of the plant's genes involved in the metabolic pathway producing the oil is a large first step to allow such utilization. A recent example of how engineering natural biochemical pathways works is Golden rice which has involved genetically modifying its pathway, so that a precursor to vitamin A is produced in large quantities making the brown-colored rice a potential solution for vitamin A deficiency.^[17] This is concept of engineering plants to do "work" is popular^[18] and its potential would dramatically increase as a result of gene information on these 1000 plant species. Biosynthetic pathways could also be used for mass production of medicinal compounds using plants rather than manual organic chemical reactions as most are created currently.

One of the most unexpected results of the project was the discovery of multiple novel light-sensitive ion-channels used extensively for optogenetic control of neurons discovered through sequencing and physiological characterization of opsins from over 100 species of alga species by the project.^[19] The characterization of these novel channelrhodopsin sequences providing resources for protein engineers who would normally have no interest in or ability to generate sequence data from these many plant species.^[20] A number of biotech companies are developing these channelrhodopsin proteins for medical purposes, with many of these optogenetic therapy candidates under clinical trials to restore vision for retinal blindness. The first published results of these treating retinitis pigmentosa coming out in July 2021.^[21]

Project approach

Sequencing was initially done on the Illumina Genome Analyzer GAII next-generation DNA sequencing platform at the Beijing Genomics Institute (BGI Shenzhen, China), but later samples were run on the faster Illumina HiSeq 2000 platform. Starting with the 28 Illumina Genome Analyzer next-generation DNA sequencing machines, these were eventually upgraded to 100 HiSeq 2000 sequencers at the Beijing Genomics Institute. The initial 3Gb/run (3 billion base pairs per experiment) capacity of each of these machines enabled fast and accurate sequencing of the plant samples.^[22]

Species selection

The selection of plant species to be sequenced was compiled through an international collaboration of the various funding agencies and researcher groups expressing their interest in certain plants.^[1] There was a focus on those plant species that are known to have useful biosynthetic capacity to facilitate the biotechnology goals of the project, and selection of other species to fill in gaps and explain some unknown evolutionary relationships of the current plant phylogeny. In addition to industrial compound biosynthetic capacity, plant species known or suspected to produce medically active chemicals (such as poppies producing opiates) were assigned a high priority to better understand the synthesis process, explore commercial production potential, and discover new pharmaceutical options. A large number of plant species with medicinal properties were selected from traditional Chinese medicine (TCM).^[1] The completed list of selected species can be publicly viewed on the website,^[23] and methodological details and data access details have been published in detail.^[5]^[24]

Transcriptome vs. genome sequencing

Rather than sequencing the entire genome (all DNA sequence) of the various plant species, the project sequenced only those regions of the genome that produce a protein product (coding genes); the transcriptome.^[1] This approach is justified by the focus on biochemical pathways where only the genes producing the involved proteins are required to understand the synthetic mechanism, and because these thousands of sequences would represent adequate sequence detail to construct very robust evolutionary relationships through sequence comparison. The numbers of coding genes in plant species can vary considerably, but all have tens of thousands or more making the transcriptome a large collection of information. However, non-coding sequence makes up the majority (>90%) of the genome content.^[25] Although this approach is similar conceptually to expressed sequence tags (ESTs), it is fundamentally different in that the entire sequence of each gene will be acquired with high coverage rather than just a small portion of the gene sequence with an EST.^[26] To distinguish the two, the non-EST method is known as "shotgun transcriptome sequencing".^[26]

Transcriptome shotgun sequencing

mRNA (messenger RNA) is collected from a sample, converted to cDNA by a reverse transcriptase enzyme, and then fragmented so that it can be sequenced.^[1]^[22] Other than transcriptome shotgun sequencing, this technique has been called RNA-seq and whole transcriptome shotgun sequencing (WTSS).^[26] Once the cDNA fragments are sequenced, they will be de novo assembled (without aligning to a reference genome sequence) back into the complete gene sequence by combining all of the fragments from that gene during the data analysis phase. A new a de novo transcriptome assembler designed specifically for RNA-Seq was produced for this project,^[27] SOAPdenovo-Trans being part of the SOAP suite of genome assembly tools from the BGI.

Plant tissue sampling

The samples came from around the world, with a number of particularly rare species being supplied by botanical gardens such as the Fairy Lake Botanical Garden (Shenzhen, China).^{[citation needed]} The type of tissue collected was determined by the expected location of biosynthetic activity; for example if an interesting process or chemical is known to exist primarily in the leaves, leaf sample was used. A number of RNA-sequencing protocols were adapted and tested for different tissue types,^[24] and these were openly shared via the protocols.io platform.^[28]

Potential limitations

Since only the transcriptome was sequenced, the project did not reveal information about gene regulatory sequence, non-coding RNAs, DNA repetitive elements, or other genomic features that are not part of the coding sequence. Based on the few whole plant genomes collected so far, these non-coding regions will in fact make up the majority of the genome,^[25]^[29] and the non-coding DNA may actually be the primary driver of trait differences seen between species.^[30]

Since mRNA was the starting material, the amount of sequence representation for a given gene is based on the expression level (how many mRNA molecules it produces). This means that highly expressed genes get better coverage because there is more sequence to work from.^[30] The result, then, is that some important genes may not have been reliably detected by the project if they are expressed at a low level yet still have important biochemical functions.

Many plant species (especially agriculturally manipulated ones) ^[29] are known to have undergone large genome-wide changes through duplication of the whole genome. The rice and the wheat genomes, for example, can have 4-6 copies of whole genomes ^[29] (wheat) whereas animals typically only have 2 (diploidy). These duplicated genes may pose a problem for the de novo assembly of sequence fragments, because repeat sequences confuse the computer programs when trying to put the fragments together, and they can be difficult to track through evolution.

Comparison with the 1000 Genomes Project

Similarities

Just as the Beijing Genomics Institute in Shenzhen, China is one of the major genomics centers involved in the 1000 Genomes Project, the institute is the site of sequencing for the 1000 Plant Genomes Project.^[31] Both projects are large-scale efforts to obtain detailed DNA sequence information to improve our understanding of the organisms, and both projects will utilize next-generation sequencing to facilitate a timely completion.

Differences

The goals of the two projects are significantly different. While the 1000 Genomes Project focuses on genetic variation in a single species, the 1000 Plant Genomes Project looks at the evolutionary relationships and genes of 1000 different plant species.

While the 1000 Genomes Project was estimated to cost up to $50 million USD,^[6] the 1000 Plant Genomes Project was not as expensive; the difference in cost coming from the target sequence in the genomes.^[1] Since the 1000 Plant Genomes Project only sequenced the transcriptome, whereas the human project sequenced as much of the genome as is decided feasible,^[6] there is a much lower amount of sequencing effort needed in this more specific approach. While this means that there was less overall sequence output relative to the 1000 Genomes Project, the non-coding portions of the genomes excluded in the 1000 Plant Genomes Project were not as important to its goals like they are to the human project. So then the more focused approach of the 1000 Plant Genomes Project minimized cost while still achieving its goals.

Funding

The project was funded by Alberta Innovates - Technology Futures (merger of iCORE [1]), Genome Alberta, the University of Alberta, the Beijing Genomics Institute (BGI), and Musea Ventures (a USA-based private investment firm).^[32] To date, the project received $1.5 million CAD from the Alberta Government and another $0.5 million from Musea Ventures.^[32] In January 2010, BGI announced that it would be contributing $100 million to large-scale sequencing projects of plants and animals (including the 1000 Plant Genomes Project, and then following on to the 10,000 Plant Genome Project^[8]).^[31]

Related projects

The 1000 Genomes Project – A Deep Catalog of Human Genetic Variation^[6]
The 1001 Genomes Project – Sequencing the whole genome of 1,001 Arabidopsis strains^[33]
Genome 10K – Whole genome sequence of 10,000 vertebrate species^[34]

References

^ ^a ^b ^c ^d ^e ^f ^g ^h Retrieved Feb. 25, 2010
^ Matasci N, Hung LH, Yan Z, Carpenter EJ, Wickett NJ, Mirarab S, et al. (2014). "Data access for the 1,000 Plants (1KP) project". GigaScience. 3 (17): 17. doi:10.1186/2047-217X-3-17. PMC 4306014. PMID 25625010.
^ One Thousand Plant Transcriptomes Initiative (October 2019). "One thousand plant transcriptomes and the phylogenomics of green plants". Nature. 574 (7780): 679–685. doi:10.1038/s41586-019-1693-2. PMC 6872490. PMID 31645766.
^ Wong GK, Soltis DE, Leebens-Mack J, Wickett NJ, Barker MS, de Peer YV, et al. (May 4, 2016). "Sequencing and Analyzing the Transcriptomes of a Thousand Species Across the Tree of Life for Green Plants". Annual Review of Plant Biology. 71: 741–765. doi:10.1146/annurev-arplant-042916-041040. ISSN 1543-5008. PMID 31851546. S2CID 209416841.
^ ^a ^b Carpenter EJ, Matasci N, Ayyampalayam S, Wu S, Sun J, Yu J, et al. (October 2019). "Access to RNA-sequencing data from 1,173 plant species: The 1000 Plant transcriptomes initiative (1KP)". GigaScience. 8 (10). doi:10.1093/gigascience/giz126. PMC 6808545. PMID 31644802.
^ ^a ^b ^c ^d Hayden EC (January 2008). "International genome project launched". Nature. 451 (7177): 378–9. Bibcode:2008Natur.451R.378C. doi:10.1038/451378b. PMID 18216809. S2CID 205035320.
^ "About IGSR and the 1000 Genomes Project". IGSR: The International Genome Sample Resource. Retrieved October 2, 2018.
^ ^a ^b Cheng S, Melkonian M, Smith SA, Brockington S, Archibald JM, Delaux P, et al. (March 1, 2018). "10KP: A phylodiverse genome sequencing plan". GigaScience. 7 (3): 1–9. doi:10.1093/gigascience/giy013. PMC 5869286. PMID 29618049.
^ Lewin HA, Robinson GE, Kress WJ, Baker WJ, Coddington J, Crandall KA, et al. (April 24, 2018). "Earth BioGenome Project: Sequencing life for the future of life". Proceedings of the National Academy of Sciences. 115 (17): 4325–4333. Bibcode:2018PNAS..115.4325L. doi:10.1073/pnas.1720115115. ISSN 0027-8424. PMC 5924910. PMID 29686065.
^ Pitman NC, Jørgensen PM (November 2002). "Estimating the size of the world's threatened flora". Science. 298 (5595): 989. doi:10.1126/science.298.5595.989. PMID 12411696. S2CID 891010.
^ "NCBI Taxonomy". NCBI. Retrieved April 11, 2012.
^ Bremer K (1985). "Summary of Green Plant Phylogeny and Classification". Cladistics. 1 (4): 369–385. doi:10.1111/j.1096-0031.1985.tb00434.x. PMID 34965683. S2CID 84961691.
^ Graham LE, Delwiche CF, Mishler BD (1991). "Phylogenetic connections between the'green algae'and the'bryophytes'". Advances in Bryology. 213–44 (3): 451–483. JSTOR 2399900.
^ ^a ^b Doyle JJ (January 1992). "Gene trees and species trees: molecular systematics as one-character taxonomy". Systematic Botany. 1 (1): 144–63. doi:10.2307/2419070. JSTOR 2419070.
^ Li Z, Barker MS (February 1, 2020). "Inferring putative ancient whole-genome duplications in the 1000 Plants (1KP) initiative: access to gene family phylogenies and age distributions". GigaScience. 9 (2). doi:10.1093/gigascience/giaa004. PMC 7011446. PMID 32043527.
^ Augustus GD, Jayabalan M, Rajarathinam K, Ray AK, Seiler GJ (2002). "Potential hydrocarbon producing species of Western Ghats, Tamil Nadu, India". Biomass and Bioenergy. 23 (3): 165–169. Bibcode:2002BmBe...23..165A. doi:10.1016/S0961-9534(02)00045-4.
^ Ye X, Al-Babili S, Klöti A, Zhang J, Lucca P, Beyer P, et al. (January 2000). "Engineering the provitamin A (beta-carotene) biosynthetic pathway into (carotenoid-free) rice endosperm". Science. 287 (5451): 303–5. Bibcode:2000Sci...287..303Y. doi:10.1126/science.287.5451.303. PMID 10634784. S2CID 40258379.
^ Taiz L, Zeiger E (2006). "Chapter 13: Secondary metabolites and plant defense". Plant physiology (4th ed.). Sinauer Associates. ISBN 978-0-87893-856-8.
^ Klapoetke NC, Murata Y, Kim SS, Pulver SR, Birdsey-Benson A, Cho YK, et al. (March 2014). "Independent optical excitation of distinct neural populations". Nature Methods. 11 (3): 338–346. doi:10.1038/nmeth.2836. PMC 3943671. PMID 24509633.
^ Wong GK, Soltis DE, Leebens-Mack J, Wickett NJ, Barker MS, Van de Peer Y, et al. (April 2020). "Sequencing and Analyzing the Transcriptomes of a Thousand Species Across the Tree of Life for Green Plants". Annual Review of Plant Biology. 71: 741–765. doi:10.1146/annurev-arplant-042916-041040. PMID 31851546. S2CID 209416841.
^ Sahel JA, Boulanger-Scemama E, Pagot C, Arleo A, Galluppi F, Martel JN, et al. (July 2021). "Partial recovery of visual function in a blind patient after optogenetic therapy". Nature Methods. 27 (7): 1223–1229. doi:10.1038/s41591-021-01351-4. PMID 34031601. S2CID 235203605.
^ ^a ^b "Retrieved Feb. 25, 2010". Archived from the original on March 7, 2010. Retrieved March 3, 2010.
^ "1kP Sample List Viewer". www.onekp.com. Retrieved April 10, 2020.
^ ^a ^b Johnson MT, Carpenter EJ, Tian Z, Bruskiewich R, Burris JN, Carrigan CT, et al. (November 21, 2012). "Evaluating Methods for Isolating Total RNA and Predicting the Success of Sequencing Phylogenetically Diverse Plant Transcriptomes". PLOS ONE. 7 (11): e50226. Bibcode:2012PLoSO...750226J. doi:10.1371/journal.pone.0050226. ISSN 1932-6203. PMC 3504007. PMID 23185583.
^ ^a ^b Morgante M (April 2006). "Plant genome organisation and diversity: the year of the junk!". Current Opinion in Biotechnology. 17 (2): 168–73. doi:10.1016/j.copbio.2006.03.001. PMID 16530402.
^ ^a ^b ^c Morozova O, Hirst M, Marra MA (2009). "Applications of new sequencing technologies for transcriptome analysis". Annual Review of Genomics and Human Genetics. 10: 135–51. doi:10.1146/annurev-genom-082908-145957. PMID 19715439. S2CID 26713396.
^ Xie Y, Wu G, Tang J, Luo R, Patterson J, Liu S, et al. (June 15, 2014). "SOAPdenovo-Trans: de novo transcriptome assembly with short RNA-Seq reads". Bioinformatics. 30 (12): 1660–1666. arXiv:1305.6760. doi:10.1093/bioinformatics/btu077. ISSN 1367-4803. PMID 24532719.
^ T M, J E, Tian Z, Bruskiewich R, N J, T C, et al. (August 15, 2019). "RNA Isolation from Plant Tissue v1 (protocols.io.439gyr6)". Protocols.io. doi:10.17504/protocols.io.439gyr6.
^ ^a ^b ^c Yu J, Hu S, Wang J, Wong GK, Li S, Liu B, et al. (April 2002). "A draft sequence of the rice genome (Oryza sativa L. ssp. indica)". Science. 296 (5565): 79–92. Bibcode:2002Sci...296...79Y. doi:10.1126/science.1068037. PMID 11935017. S2CID 208529258.
^ ^a ^b Bird CP, Stranger BE, Liu M, Thomas DJ, Ingle CE, Beazley C, et al. (2007). "Fast-evolving noncoding sequences in the human genome". Genome Biology. 8 (6): R118. doi:10.1186/gb-2007-8-6-r118. PMC 2394770. PMID 17578567.
^ ^a ^b "BGI Seeks Proposals to Sequence 1,000 Plant, Animal Genomes; Pledges $100M Toward Effort". GenomeWeb. January 12, 2010. Retrieved February 25, 2010.
^ ^a ^b "Alberta iCORE researcher leads international genome project". Government of Alberta. November 13, 2008. Archived from the original on September 25, 2012. Retrieved August 21, 2018.
^ Weigel D, Mott R (2009). "The 1001 genomes project for Arabidopsis thaliana". Genome Biology. 10 (5): 107. doi:10.1186/gb-2009-10-5-107. PMC 2718507. PMID 19519932.
^ Genome 10K Community of Scientists (2009). "Genome 10K: a proposal to obtain whole-genome sequence for 10,000 vertebrate species". The Journal of Heredity. 100 (6): 659–74. doi:10.1093/jhered/esp086. PMC 2877544. PMID 19892720.{{cite journal}}: CS1 maint: numeric names: authors list (link)

External links

Official website

This page was last edited on 10 February 2024, at 00:22

From Wikipedia, the free encyclopedia