< crisprAllTargets | This track shows the DNA sequences targetable by CRISPR RNA guides using < crisprAllTargets | the Cas9 enzyme from S. pyogenes (PAM: NGG) over the entire < crisprAllTargets | X. tropicalis (xenTro10) genome. CRISPR target sites were annotated with < crisprAllTargets | predicted specificity (off-target effects) and predicted efficiency < crisprAllTargets | (on-target cleavage) by various < crisprAllTargets | algorithms through the tool CRISPOR. Sp-Cas9 usually cuts double-stranded DNA three or < crisprAllTargets | four base pairs 5' of the PAM site. < crisprAllTargets |
< crisprAllTargets | < crisprAllTargets |< crisprAllTargets | The track "CRISPR Targets" shows all potential -NGG target sites across the genome. < crisprAllTargets | The target sequence of the guide is shown with a thick (exon) bar. The PAM < crisprAllTargets | motif match (NGG) is shown with a thinner bar. Guides < crisprAllTargets | are colored to reflect both predicted specificity and efficiency. Specificity < crisprAllTargets | reflects the "uniqueness" of a 20mer sequence in the genome; the less unique a < crisprAllTargets | sequence is, the more likely it is to cleave other locations of the genome < crisprAllTargets | (off-target effects). Efficiency is the frequency of cleavage at the target < crisprAllTargets | site (on-target efficiency).
< crisprAllTargets | < crisprAllTargets |Shades of gray stand for sites that are hard to target specifically, as the < crisprAllTargets | 20mer is not very unique in the genome:
< crisprAllTargets || impossible to target: target site has at least one identical copy in the genome and was not scored | |
| hard to target: many similar sequences in the genome that alignment stopped, repeat? | |
| hard to target: target site was aligned but results in a low specificity score <= 50 (see below) |
Colors highlight targets that are specific in the genome (MIT specificity > 50) but have different predicted efficiencies:
< crisprAllTargets || unable to calculate Doench/Fusi 2016 efficiency score | |
| low predicted cleavage: Doench/Fusi 2016 Efficiency percentile <= 30 | |
| medium predicted cleavage: Doench/Fusi 2016 Efficiency percentile > 30 and < 55 | |
| high predicted cleavage: Doench/Fusi 2016 Efficiency > 55 |
< crisprAllTargets | Mouse-over a target site to show predicted specificity and efficiency scores:
< crisprAllTargets |
Click onto features to show all scores and predicted off-targets with up to < crisprAllTargets | four mismatches. The Out-of-Frame score by Bae et al. 2014 < crisprAllTargets | is correlated with < crisprAllTargets | the probability that mutations induced by the guide RNA will disrupt the open < crisprAllTargets | reading frame. The authors recommend out-of-frame scores > 66 to create < crisprAllTargets | knock-outs with a single guide efficiently.
< crisprAllTargets | < crisprAllTargets |
Off-target sites are sorted by the CFD (Cutting Frequency Determination) < crisprAllTargets | score (Doench et al. 2016). < crisprAllTargets | The higher the CFD score, the more likely there is off-target cleavage at that site. < crisprAllTargets | Off-targets with a CFD score < 0.023 are not shown on this page, but are available when < crisprAllTargets | following the link to the external CRISPOR tool. < crisprAllTargets | When compared against experimentally validated off-targets by < crisprAllTargets | Haeussler et al. 2016, the large majority of predicted < crisprAllTargets | off-targets with CFD scores < 0.023 were false-positives. For storage and performance < crisprAllTargets | reasons, on the level of individual off-targets, only CFD scores are available.
< crisprAllTargets | < crisprAllTargets |< crisprAllTargets | Like most algorithms, the MIT specificity score is not always a perfect < crisprAllTargets | predictor of off-target effects. Despite low scores, many tested guides < crisprAllTargets | caused few and/or weak off-target cleavage when tested with whole-genome assays < crisprAllTargets | (Figure 2 from Haeussler < crisprAllTargets | et al. 2016), as shown below, and the published data contains few data points < crisprAllTargets | with high specificity scores. Overall though, the assays showed that the higher < crisprAllTargets | the specificity score, the lower the off-target effects.
< crisprAllTargets | < crisprAllTargets |
< crisprAllTargets |
< crisprAllTargets | Similarly, efficiency scoring is not very accurate: guides with low < crisprAllTargets | scores can be efficient and vice versa. As a general rule, however, the higher < crisprAllTargets | the score, the less likely that a guide is very inefficient. The < crisprAllTargets | following histograms illustrate, for each type of score, how the share of < crisprAllTargets | inefficient guides drops with increasing efficiency scores: < crisprAllTargets |
< crisprAllTargets | < crisprAllTargets |
< crisprAllTargets |
< crisprAllTargets | When reading this plot, keep in mind that both scores were evaluated on < crisprAllTargets | their own training data. Especially for the Moreno-Mateos score, the < crisprAllTargets | results are too optimistic, due to overfitting. When evaluated on independent < crisprAllTargets | datasets, the correlation of the prediction with other assays was around 25% < crisprAllTargets | lower, see Haeussler et al. 2016. At the time of < crisprAllTargets | writing, there is no independent dataset available yet to determine the < crisprAllTargets | Moreno-Mateos accuracy for each score percentile range.
< crisprAllTargets | < crisprAllTargets |< crisprAllTargets | The entire X. tropicalis (xenTro10) genome was scanned for the -NGG motif. Flanking 20mer < crisprAllTargets | guide sequences were < crisprAllTargets | aligned to the genome with BWA and scored with MIT Specificity scores using the < crisprAllTargets | command-line version of crispor.org. Non-unique guide sequences were skipped. < crisprAllTargets | Flanking sequences were extracted from the genome and input for Crispor < crisprAllTargets | efficiency scoring, available from the Crispor downloads page, which < crisprAllTargets | includes the Doench 2016, Moreno-Mateos 2015 and Bae < crisprAllTargets | 2014 algorithms, among others.
< crisprAllTargets |< crisprAllTargets | Note that the Doench 2016 scores were updated by < crisprAllTargets | the Broad institute in 2017 ("Azimuth" update). As a result, earlier versions of < crisprAllTargets | the track show the old Doench 2016 scores and this version of the track shows new < crisprAllTargets | Doench 2016 scores. Old and new scores are almost identical, they are < crisprAllTargets | correlated to 0.99 and for more than 80% of the guides the difference is below 0.02. < crisprAllTargets | However, for very few guides, the difference can be bigger. In case of doubt, we recommend < crisprAllTargets | the new scores. Crispor.org can display both < crisprAllTargets | scores and many more with the "Show all scores" link.
< crisprAllTargets | < crisprAllTargets |< crisprAllTargets | Positional data can be explored interactively with the < crisprAllTargets | Table < crisprAllTargets | Browser or the Data Integrator. < crisprAllTargets | For small programmatic positional queries, the track can be accessed using our < crisprAllTargets | REST API. For genome-wide data or < crisprAllTargets | automated analysis, CRISPR genome annotations can be downloaded from < crisprAllTargets | our download server < crisprAllTargets | as a bigBedFile.
< crisprAllTargets |< crisprAllTargets | The files for this track are called crispr.bb, which lists positions and < crisprAllTargets | scores, and crisprDetails.tab, which has information about off-target matches. Individual < crisprAllTargets | regions or whole genome annotations can be obtained using our tool bigBedToBed, < crisprAllTargets | which can be compiled from the source code or downloaded as a pre-compiled < crisprAllTargets | binary for your system. Instructions for downloading source code and binaries can be found < crisprAllTargets | here. The tool < crisprAllTargets | can also be used to obtain only features within a given range, e.g.
< crisprAllTargets |< crisprAllTargets | bigBedToBed < crisprAllTargets | http://hgdownload.soe.ucsc.edu/gbdb/xenTro10/crisprAllTargets/crispr.bb -chrom=chr21 < crisprAllTargets | -start=0 -end=1000000 stdout
< crisprAllTargets | < crisprAllTargets |< crisprAllTargets | Track created by Maximilian Haeussler, with helpful input < crisprAllTargets | from Jean-Paul Concordet (MNHN Paris) and Alberto Stolfi (NYU). < crisprAllTargets |
< crisprAllTargets | < crisprAllTargets |< crisprAllTargets | Haeussler M, Schönig K, Eckert H, Eschstruth A, Mianné J, Renaud JB, Schneider-Maunoury S, < crisprAllTargets | Shkumatava A, Teboul L, Kent J et al. < crisprAllTargets | Evaluation of off-target and on-target scoring algorithms and integration into the < crisprAllTargets | guide RNA selection tool CRISPOR. < crisprAllTargets | Genome Biol. 2016 Jul 5;17(1):148. < crisprAllTargets | PMID: 27380939; PMC: PMC4934014 < crisprAllTargets |
< crisprAllTargets | < crisprAllTargets |< crisprAllTargets | Bae S, Kweon J, Kim HS, Kim JS. < crisprAllTargets | < crisprAllTargets | Microhomology-based choice of Cas9 nuclease target sites. < crisprAllTargets | Nat Methods. 2014 Jul;11(7):705-6. < crisprAllTargets | PMID: 24972169 < crisprAllTargets |
< crisprAllTargets | < crisprAllTargets |< crisprAllTargets | Doench JG, Fusi N, Sullender M, Hegde M, Vaimberg EW, Donovan KF, Smith I, Tothova Z, Wilen C, < crisprAllTargets | Orchard R et al. < crisprAllTargets | < crisprAllTargets | Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. < crisprAllTargets | Nat Biotechnol. 2016 Feb;34(2):184-91. < crisprAllTargets | PMID: 26780180; PMC: PMC4744125 < crisprAllTargets |
< crisprAllTargets | < crisprAllTargets |< crisprAllTargets | Hsu PD, Scott DA, Weinstein JA, Ran FA, Konermann S, Agarwala V, Li Y, Fine EJ, Wu X, Shalem O < crisprAllTargets | et al. < crisprAllTargets | < crisprAllTargets | DNA targeting specificity of RNA-guided Cas9 nucleases. < crisprAllTargets | Nat Biotechnol. 2013 Sep;31(9):827-32. < crisprAllTargets | PMID: 23873081; PMC: PMC3969858 < crisprAllTargets |
< crisprAllTargets | < crisprAllTargets |< crisprAllTargets | Moreno-Mateos MA, Vejnar CE, Beaudoin JD, Fernandez JP, Mis EK, Khokha MK, Giraldez AJ. < crisprAllTargets | < crisprAllTargets | CRISPRscan: designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo. < crisprAllTargets | Nat Methods. 2015 Oct;12(10):982-8. < crisprAllTargets | PMID: 26322839; PMC: PMC4589495 < crisprAllTargets |
< crisprAllTargets | 2415,2418d2200 < ncbiRefSeqGenomicDiff | html < ncbiRefSeqGenomicDiff | < ncbiRefSeqOther | html < ncbiRefSeqOther | 3542,3554d3323 < rmsk |< rmsk | When analyzing the data tables of this track, keep in mind that Repbase is not the same < rmsk | as the Repeatmasker sequence database and that the repeat names in the < rmsk | Repeatmasker output are not the same as the sequence names in the Repeatmasker < rmsk | database. Concretely, you can find a name such as "L1PA4" in the Repeatmasker < rmsk | output and this track, but there is not necessarily a single sequence "L1PA4" < rmsk | in the Repeatmasker database. This is because Repeatmasker creates annotations < rmsk | by joining matches to partial pieces of the database together so there is no < rmsk | 1:1 relationship between its sequence database and the annotations. To learn < rmsk | more, you can read the Repeatmasker paper, its source code or reach out to the < rmsk | Repeatmasker authors, your local expert on transposable elements or us. < rmsk |
< rmsk | 3709,4074d3477 < unipAliSwissprot | html < unipAliSwissprot | < unipAliTrembl | html < unipAliTrembl | < unipChain | html < unipChain | < unipConflict | html < unipConflict | < unipDisulfBond | html < unipDisulfBond | < unipDomain | html < unipDomain | < unipInterest | html < unipInterest | < unipLocCytopl | html < unipLocCytopl | < unipLocExtra | html < unipLocExtra | < unipLocSignal | html < unipLocSignal | < unipLocTransMemb | html < unipLocTransMemb | < unipModif | html < unipModif | < unipMut | html < unipMut | < unipOther | html < unipOther | < unipRepeat | html < unipRepeat | < uniprot | html < uniprot |< uniprot | This track shows protein sequences and annotations on them from the UniProt/SwissProt database, < uniprot | mapped to genomic coordinates. < uniprot |
< uniprot |< uniprot | UniProt/SwissProt data has been curated from scientific publications by the UniProt staff, < uniprot | UniProt/TrEMBL data has been predicted by various computational algorithms. < uniprot | The annotations are divided into multiple subtracks, based on their "feature type" in UniProt. < uniprot | The first two subtracks below - one for SwissProt, one for TrEMBL - show the < uniprot | alignments of protein sequences to the genome, all other tracks below are the protein annotations < uniprot | mapped through these alignments to the genome. < uniprot |
< uniprot | < uniprot || Track Name | < uniprot |Description | < uniprot |
|---|---|
| UCSC Alignment, SwissProt = curated protein sequences | < uniprot |Protein sequences from SwissProt mapped to the genome. All other < uniprot | tracks are (start,end) SwissProt annotations on these sequences mapped < uniprot | through this alignment. Even protein sequences without a single curated < uniprot | annotation (splice isoforms) are visible in this track. Each UniProt protein < uniprot | has one main isoform, which is colored in dark. Alternative isoforms are < uniprot | sequences that do not have annotations on them and are colored in light-blue. < uniprot | They can be hidden with the TrEMBL/Isoform filter (see below). |
| UCSC Alignment, TrEMBL = predicted protein sequences | < uniprot |Protein sequences from TrEMBL mapped to the genome. All other tracks < uniprot | below are (start,end) TrEMBL annotations mapped to the genome using < uniprot | this track. This track is hidden by default. To show it, click its < uniprot | checkbox on the track configuration page. |
| UniProt Signal Peptides | < uniprot |Regions found in proteins destined to be secreted, generally cleaved from mature protein. | < uniprot |
| UniProt Extracellular Domains | < uniprot |Protein domains with the comment "Extracellular". | < uniprot |
| UniProt Transmembrane Domains | < uniprot |Protein domains of the type "Transmembrane". | < uniprot |
| UniProt Cytoplasmic Domains | < uniprot |Protein domains with the comment "Cytoplasmic". | < uniprot |
| UniProt Polypeptide Chains | < uniprot |Polypeptide chain in mature protein after post-processing. | < uniprot |
| UniProt Regions of Interest | < uniprot |Regions that have been experimentally defined, such as the role of a region in mediating protein-protein interactions or some other biological process. | < uniprot |
| UniProt Domains | < uniprot |Protein domains, zinc finger regions and topological domains. | < uniprot |
| UniProt Disulfide Bonds | < uniprot |Disulfide bonds. | < uniprot |
| UniProt Amino Acid Modifications | < uniprot |Glycosylation sites, modified residues and lipid moiety-binding regions. | < uniprot |
| UniProt Amino Acid Mutations | < uniprot |Mutagenesis sites and sequence variants. | < uniprot |
| UniProt Protein Primary/Secondary Structure Annotations | < uniprot |Beta strands, helices, coiled-coil regions and turns. | < uniprot |
| UniProt Sequence Conflicts | < uniprot |Differences between Genbank sequences and the UniProt sequence. | < uniprot |
| UniProt Repeats | < uniprot |Regions of repeated sequence motifs or repeated domains. | < uniprot |
| UniProt Other Annotations | < uniprot |All other annotations, e.g. compositional bias | < uniprot |
< uniprot | For consistency and convenience for users of mutation-related tracks, < uniprot | the subtrack "UniProt/SwissProt Variants" is a copy of the track < uniprot | "UniProt Variants" in the track group "Phenotype and Literature", or < uniprot | "Variation and Repeats", depending on the assembly. < uniprot |
< uniprot | < uniprot |< uniprot | Genomic locations of UniProt/SwissProt annotations are labeled with a short name for < uniprot | the type of annotation (e.g. "glyco", "disulf bond", "Signal peptide" < uniprot | etc.). A click on them shows the full annotation and provides a link to the UniProt/SwissProt < uniprot | record for more details. TrEMBL annotations are always shown in < uniprot | light blue, except in the Signal Peptides, < uniprot | Extracellular Domains, Transmembrane Domains, and Cytoplamsic domains subtracks.
< uniprot | < uniprot |< uniprot | Mouse over a feature to see the full UniProt annotation comment. For variants, the mouse over will < uniprot | show the full name of the UniProt disease acronym. < uniprot |
< uniprot | < uniprot |< uniprot | The subtracks for domains related to subcellular location are sorted from outside to inside of < uniprot | the cell: Signal peptide, < uniprot | extracellular, < uniprot | transmembrane, and cytoplasmic. < uniprot |
< uniprot | < uniprot |< uniprot | Features in the "UniProt Modifications" (modified residues) track are drawn in < uniprot | light green. Disulfide bonds are shown in < uniprot | dark grey. Topological domains < uniprot | in maroon and zinc finger regions in < uniprot | olive green. < uniprot |
< uniprot | < uniprot |< uniprot | Duplicate annotations are removed as far as possible: if a TrEMBL annotation < uniprot | has the same genome position and same feature type, comment, disease and < uniprot | mutated amino acids as a SwissProt annotation, it is not shown again. Two < uniprot | annotations mapped through different protein sequence alignments but with the same genome < uniprot | coordinates are only shown once.
< uniprot | < uniprot |On the configuration page of this track, you can choose to hide any TrEMBL annotations. < uniprot | This filter will also hide the UniProt alternative isoform protein sequences because < uniprot | both types of information are less relevant to most users. Please contact us if you < uniprot | want more detailed filtering features.
< uniprot | < uniprot |Note that for the human hg38 assembly and SwissProt annotations, there < uniprot | also is a public < uniprot | track hub prepared by UniProt itself, with < uniprot | genome annotations maintained by UniProt using their own mapping < uniprot | method based on those Gencode/Ensembl gene models that are annotated in UniProt < uniprot | for a given protein. For proteins that differ from the genome, UniProt's mapping method < uniprot | will, in most cases, map a protein and its annotations to an unexpected location < uniprot | (see below for details on UCSC's mapping method).
< uniprot | < uniprot |< uniprot | Briefly, UniProt protein sequences were aligned to the transcripts associated < uniprot | with the protein, the top-scoring alignments were retained, and the result was < uniprot | projected to the genome through a transcript-to-genome alignment. < uniprot | Depending on the genome, the transcript-genome alignments was either < uniprot | provided by the source database (NBCI RefSeq), created at UCSC (UCSC RefSeq) or < uniprot | derived from the transcripts (Ensembl/Augustus). The transcript set is NCBI < uniprot | RefSeq for hg38, UCSC RefSeq for hg19 (due to alt/fix haplotype misplacements < uniprot | in the NCBI RefSeq set on hg19). For other genomes, RefSeq, Ensembl and Augustus < uniprot | are tried, in this order. The resulting protein-genome alignments of this process < uniprot | are available in the file formats for liftOver or pslMap from our data archive < uniprot | (see "Data Access" section below). < uniprot |
< uniprot | < uniprot |An important step of the mapping process protein -> transcript -> < uniprot | genome is filtering the alignment from protein to transcript. Due to < uniprot | differences between the UniProt proteins and the transcripts (proteins were < uniprot | made many years before the transcripts were made, and human genomes have < uniprot | variants), the transcript with the highest BLAST score when aligning the < uniprot | protein to all transcripts is not always the correct transcript for a protein < uniprot | sequence. Therefore, the protein sequence is aligned to only a very short list < uniprot | of one or sometimes more transcripts, selected by a three-step procedure: < uniprot |
< uniprot | For strategy 2 and 3, many of the transcripts found do not differ in coding < uniprot | sequence, so the resulting alignments on the genome will be identical. < uniprot | Therefore, any identical alignments are removed in a final filtering step. The < uniprot | details page of these alignments will contain a list of all transcripts that < uniprot | result in the same protein-genome alignment. On hg38, only a handful of edge < uniprot | cases (pseudogenes, very recently added proteins) remain in 2023 where strategy < uniprot | 3 has to be used.
< uniprot | < uniprot |In other words, when an NCBI or UCSC RefSeq track is used for the mapping and to align a < uniprot | protein sequence to the correct transcript, we use a three stage process: < uniprot |
This system was designed to resolve the problem of incorrect mappings of < uniprot | proteins, mostly on hg38, due to differences between the SwissProt < uniprot | sequences and the genome reference sequence, which has changed since the < uniprot | proteins were defined. The problem is most pronounced for gene families < uniprot | composed of either very repetitive or very similar proteins. To make sure that < uniprot | the alignments always go to the best chromosome location, all _alt and _fix < uniprot | reference patch sequences are ignored for the alignment, so the patches are < uniprot | entirely free of UniProt annotations. Please contact us if you have feedback on < uniprot | this process or example edge cases. We are not aware of a way to evaluate the < uniprot | results completely and in an automated manner.
< uniprot |< uniprot | Proteins were aligned to transcripts with TBLASTN, converted to PSL, filtered < uniprot | with pslReps (93% query coverage, keep alignments within top 1% score), lifted to genome < uniprot | positions with pslMap and filtered again with pslReps. UniProt annotations were < uniprot | obtained from the UniProt XML file. The UniProt annotations were then mapped to the < uniprot | genome through the alignment described above using the pslMap program. This approach < uniprot | draws heavily on the LS-SNP pipeline by Mark Diekhans. < uniprot | Like all Genome Browser source code, the main script used to build this track < uniprot | can be found on Github. < uniprot |
< uniprot | < uniprot |< uniprot | This track is automatically updated on an ongoing basis, every 2-3 months. < uniprot | The current version name is always shown on the track details page, it includes the < uniprot | release of UniProt, the version of the transcript set and a unique MD5 that is < uniprot | based on the protein sequences, the transcript sequences, the mapping file < uniprot | between both and the transcript-genome alignment. The exact transcript < uniprot | that was used for the alignment is shown when clicking a protein alignment < uniprot | in one of the two alignment tracks. < uniprot |
< uniprot | < uniprot |< uniprot | For reproducibility of older analysis results and for manual inspection, previous versions of this track < uniprot | are available for browsing in the form of the UCSC UniProt Archive Track Hub (click this link to connect the hub now). The underlying data of < uniprot | all releases of this track (past and current) can be obtained from our downloads server, including the UniProt < uniprot | protein-to-genome alignment.
< uniprot | < uniprot |< uniprot | The raw data of the current track can be explored interactively with the < uniprot | Table Browser, or the < uniprot | Data Integrator. < uniprot | For automated analysis, the genome annotation is stored in a bigBed file that < uniprot | can be downloaded from the < uniprot | download server. < uniprot | The exact filenames can be found in the < uniprot | track configuration file. < uniprot | Annotations can be converted to ASCII text by our tool bigBedToBed < uniprot | which can be compiled from the source code or downloaded as a precompiled < uniprot | binary for your system. Instructions for downloading source code and binaries can be found < uniprot | here. < uniprot | The tool can also be used to obtain only features within a given range, for example: < uniprot |
< uniprot | bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/xenTro10/uniprot/unipStruct.bb -chrom=chr6 -start=0 -end=1000000 stdout < uniprot |
< uniprot | Please refer to our < uniprot | mailing list archives < uniprot | for questions, or our < uniprot | Data Access FAQ < uniprot | for more information. < uniprot | < uniprot | < uniprot |< uniprot | < uniprot |
To facilitate mapping protein coordinates to the genome, we provide the < uniprot | alignment files in formats that are suitable for our command line tools. Our < uniprot | command line programs liftOver or pslMap can be used to map < uniprot | coordinates on protein sequences to genome coordinates. The filenames are < uniprot | unipToGenome.over.chain.gz (liftOver) and unipToGenomeLift.psl.gz (pslMap).
< uniprot | < uniprot |Example commands: < uniprot |
< uniprot | wget -q https://hgdownload.soe.ucsc.edu/goldenPath/archive/hg38/uniprot/2022_03/unipToGenome.over.chain.gz < uniprot | wget -q https://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/liftOver < uniprot | chmod a+x liftOver < uniprot | echo 'Q99697 1 10 annotationOnProtein' > prot.bed < uniprot | liftOver prot.bed unipToGenome.over.chain.gz genome.bed < uniprot | cat genome.bed < uniprot |< uniprot | < uniprot | < uniprot |
< uniprot | This track was created by Maximilian Haeussler at UCSC, with a lot of input from Chris < uniprot | Lee, Mark Diekhans and Brian Raney, feedback from the UniProt staff, Alejo < uniprot | Mujica, Regeneron Pharmaceuticals and Pia Riestra, GeneDx. Thanks to UniProt for making all data < uniprot | available for download. < uniprot |
< uniprot | < uniprot |< uniprot | UniProt Consortium. < uniprot | < uniprot | Reorganizing the protein space at the Universal Protein Resource (UniProt). < uniprot | Nucleic Acids Res. 2012 Jan;40(Database issue):D71-5. < uniprot | PMID: 22102590; PMC: PMC3245120 < uniprot |
< uniprot | < uniprot |< uniprot | Yip YL, Scheib H, Diemand AV, Gattiker A, Famiglietti LM, Gasteiger E, Bairoch A. < uniprot | < uniprot | The Swiss-Prot variant page and the ModSNP database: a resource for sequence and structure < uniprot | information on human protein variants. < uniprot | Hum Mutat. 2004 May;23(5):464-70. < uniprot | PMID: 15108278 < uniprot |
< uniprot | < unipStruct | html < unipStruct |