fc360268b7f7102a5012bf5e0e813692de7ae305
lrnassar
  Fri Jan 10 10:58:47 2020 -0800
Switching the API link to be relative refs #24156

diff --git src/hg/makeDb/trackDb/human/avada.html src/hg/makeDb/trackDb/human/avada.html
index e927b11..02e08ee 100644
--- src/hg/makeDb/trackDb/human/avada.html
+++ src/hg/makeDb/trackDb/human/avada.html
@@ -1,80 +1,80 @@
 <h2>Description</h2>
 
 <p>
 This track shows the genomic positions of variants in the
 <a href="http://bejerano.stanford.edu/AVADA/" target="_blank">AVADA database</a>. 
 AVADA is a database of variants built by a machine learning software
 that analyzes full text research articles to find the gene mentions in the text that 
 look like they are most relevant for genetic diagnosis, finds variant descriptions
 and uses the genes to map the variants to the genome. For details see the 
 <a target=_blank href="https://doi.org/10.1038/s41436-019-0643-6">AVADA paper</a>.
 </p>
 <p>As the data is automatically extracted from full-text publications, it includes 
 some false positives. In the original study, out of 200 randomly selected articles,
 only 99 were considered relevant after manual curation. Ideally, the track is used
 in combination with variants found in human patients, to find relevant literature, 
 or with Genome Browser tracks of variant databases that curated a single study 
 for each variant, like our tracks for HGMD or LOVD.
 <p>
 
 <h2>Display Conventions and Configuration</h2>
 
 <p>
 Genomic locations of a variants are labeled with the variant description
 in the original next. This is not a normalized HGVS string, but the original
 text as the authors of the study described it.
 The Pubmed ID, gene and transcript for each variant are shown on the
 variant's details page, as well as the PubMed title, authors and abstract. 
 Mouse over the variants to show the gene, variant, first author, year and title.
 </p>
 
 <H2>Data access</H2>
 <p>
 The raw data can be explored interactively with the <a href="../cgi-bin/hgTables">Table Browser</a>,
 for download, intersection or correlations with other tracks. To join this track with others
 based on the chromosome positions, use the <a href="../cgi-bin/hgIntegrator">Data Integrator</a>.
 
 <p>
 For automated download and analysis, the genome annotation is stored in a bigBed file that
 can be downloaded from
 <a href="http://hgdownload.soe.ucsc.edu/gbdb/$db/bbi/" target="_blank">our download server</a>.
 The file for this track is called <tt>avada.bb</tt>. Individual
 regions or the whole genome annotation can be obtained using our tool <tt>bigBedToBed</tt>
 which can be compiled from the source code or downloaded as a precompiled
 binary for your system. Instructions for downloading source code and binaries can be found
 <a href="http://hgdownload.soe.ucsc.edu/downloads.html#utilities_downloads">here</a>.
 The tool
 can also be used to obtain only features within a given range, e.g. 
 <tt>bigBedToBed http://hgdownload.soe.ucsc.edu/gbdb/hg19/bbi/avada.bb -chrom=chr21 -start=0 -end=100000000 stdout</tt></p>
 </p>
 
 <p> For automated access, this track like all others, is also available via our
-<a href="https://api.genome.ucsc.edu">API</a>.  However, for bulk processing in
+<a href="../cgi-bin/hubApi">API</a>.  However, for bulk processing in
 pipelines, downloading the data and/or using bigBed files as described above is
 usually faster.  </p>
 
 <h2>Methods</h2>
 
 <p>
 The AVADA VCF file was reformatted at UCSC to the <a href="../goldenPath/help/bigBed.html">bigBed</a> format.
 The program that performs the conversion is available on
 <a href="https://github.com/ucscGenomeBrowser/kent/blob/master/src/hg/makeDb/avada"
 target="_blank">Github</a>. The paper reference information was added from
 MEDLINE and is used Courtesy of the U.S. National Library of Medicine, according 
 to its <a href="https://www.nlm.nih.gov/databases/download/terms_and_conditions_pubmed.html" target=_blank>
 Terms and Conditions</a>.</p>
 
 <h2>Credits</h2>
 <p>
 Thanks to Gill Bejerano and Johannes Birgmeier for making the data available.
 </p>
 
 <h2>References</h2>
 <p>
 Johannes Birgmeier, Cole A. Deisseroth, Laura E. Hayward, Luisa M. T. Galhardo, Andrew P. Tierno, Karthik A. Jagadeesh, Peter D. Stenson, David N. Cooper, Jonathan A. Bernstein, Maximilian Haeussler, and Gill Bejerano:
 <em>et al</em>.
 <a href="https://doi.org/10.1038/s41436-019-0643-6" target="_blank">
 AVADA: Towards Automated Pathogenic Variant Evidence Retrieval Directly from the Full Text Literature. </a>.
 <em>Genetics in Medicine</em>. 2019.
 PMID: <a href="https://www.ncbi.nlm.nih.gov/pubmed/31467448" target="_blank">31467448</a>
 </p>