nervous system diseases ( nsds ) represent a common class of diseases , some of which are characterized by high morbidity and disabilities . for example
, stroke causes serious long - term disabilities and has been the third leading cause of death in most developed countries for many decades ( 1 ) . other nsds , such as alzheimer 's and parkinson 's diseases , cause progressive neuron damage in the brain , severely affecting quality of life and putting great burdens on families and society ( 2 ) .
nsds are remarkably complex , and the underlying causes involve changes at multiple levels , including genome , transcriptome , epigenetic mechanisms and environmental factors ( 3 ) .
therefore , one of the primary challenges in nsd research is to uncover the precise molecular mechanisms underlying the diseases .
non - coding rnas ( ncrnas ) are a family of functional rna molecules that include micrornas ( mirnas ) , long noncoding rnas ( lncrna ) , small interfering rna ( sirna ) , small nucleolar rnas ( snorna ) , piwi - interacting rna ( pirna ) , among others ( 4 ) .
there is now overwhelming evidence that changes in ncrna expression levels have been associated with nsds ( 5 ) .
for example , mirnas ( 22 nt small ncrna molecules that repress mrna target expression ) are the hallmark of various nsds , such as epilepsy ( 6 ) , multiple sclerosis ( 7 ) and huntington disease ( 8) .
lncrnas ( > 200 nt rna molecules ) have been found in recent years to play a role in nervous system development , plasticity , evolution and disease ( 9 ) .
for example , the lncrna hotair acts as an endogenous  sponge of mir-141 , resulting in significant reduction of glioma growth ( 10 ) . to date , many ncrna - related databases have been developed , such as hmdd ( 11 ) , lncrnadisease ( 12 ) and lnc2cancer ( 13 ) .
however , a high quality and well - curated public resource specifically for nsds  ncrnas association data remains unavailable .
currently , a huge amount of ncrnas and nsds data have accumulated in a short period of time with the rapid development of this field .
however , the data are distributed among many massive articles that makes it inconvenient for researchers who want to further explore the relationship between nsds and ncrnas .
it is worth noting that researchers have made great efforts to develop databases to explore nsd - related information across several fields .
for example , the methylomedb database collects dna methylation profiles of the brain ( 14 ) , the allen brain atlas integrates gene expression data and neuroanatomical information ( 15 ) and the epilepsygene and mdpd databases present gene mutation information for epilepsy and parkinson 's disease , respectively ( 16,17 ) .
, we developed nsdna , a manually curated database of experimentally supported nsd and ncrna associations , which seeks to provide a high quality , comprehensive and specialist resource of nsd - related ncrna deregulation across various species .
the current version of nsdna documents 24 713 entries of associations between 142 nsds and 8593 ncrnas in 11 species , curated from more than 1300 articles .
we expect nsdna to serve as a useful resource for researchers who wish to further investigate the relationship between ncrnas and nsds .
to ensure a high quality database , we referred to the steps used to assemble the databases hmdd , lnc2cancer and virbase ( 11,13,18 ) .
we mainly applied the following three steps in the data collection process : collecting disease and ncrna information , searching for relevant articles and extracting useful information from the selected articles .
first , nsd terminologies were organized based upon the controlled vocabulary of the medical subject heading disease categories that contains 600 nsds ( 19 ) . the ncrna information was collected and integrated from various resources : mirna symbols were taken from mirbase ( 20 ) , lncrna symbols were taken from noncode ( 21 ) and lncrnadb ( 22 ) , sirna symbols were taken from sirecords ( 23 ) , snorna symbols were taken from snorna - lbme - db ( 24 ) and pirna symbols were taken from pirnabank ( 25 ) . in total , we collected 600 nsds and more than 600 000 ncrna terms in the first step .
we also applied the ncrna category names for those and other ncrnas , such as ribosomal rna and transfer rna .
second , we used a simple script to select all abstracts in the pubmed database using the following keyword combinations :  each nervous system disease and/or  each ncrna symbol or  ncrna category name ( 18 ) .
then , we downloaded all published literature and available supplementary files describing the associations between nsds and ncrnas .
third , we manually extracted experimentally supported ncrna  nsd associations from selected articles by at least two researchers .
after systematically reviewing more than 1300 published articles , a total of 24 713 entries representing 142 nsds and 8593 ncrnas in 11 species were manually collected .
the number of ncrna - associated entries for each species is listed in table 1 .
each entry contains detailed information , including disease and ncrna name , species , the ncrna expression pattern ( e.g. upregulated , downregulated ) , experimental methods ( e.g. microarray , real - time pcr ) , experimentally verified targets of ncrna , detected tissue ( e.g. brain , blood ) , associated treatment ( e.g. levodopa , interferon - beta ) , detailed description and corresponding literature ( pubmed i d and publication year ) .
to verify altered ncrna expression in nsds , the researchers used various experimental methods , including high - throughput and low - throughput experiments .
therefore , we divided the data into high - throughput and low - throughput experiments , as previously described ( 26 ) .
high - throughput experiments include microarray and next - generation sequencing , whereas low - throughput experiments include real - time pcr , northern blot , western blot and luciferase reporter assays , and so on .
nsdna provides a user - friendly web interface for an easy database query ( figure 1 ) . in the  browse page , users can browse by mirnas , lncrnas , sirnas , snornas , pirnas , diseases , species and tissue name . by clicking on a particular node
search page , nsdna allows users to search by mirnas , lncrnas , sirnas , snornas , pirnas , disease and tissue name .
nsdna enables users to select all or particular species from the species pull - down menu .
in addition , nsdna provides an option that allows users to select low - throughput ( real - time pcr , northern blot , luciferase reporter assay , etc . ) or high - throughput ( microarray , next - generation sequencing , etc . ) experimental data or both .
the search results are organized as tables in the web interface , and users can obtain more extensive information by clicking on the  details hyperlink . nsdna supports 
in addition , nsdna invites users to submit novel nsd - ncrna associations for updating the database in the  submit page .
once approved by the submission review committee , the submitted record is included in the database and made available to the public in the following release . we provide a detailed database use tutorial in the 
moreover , nsdna provides users a prioritization page that enables researchers to prioritize ncrnas in a specific tissue and disease . for mirnas , we used low - throughput experiments confirmed mirna as the gold standard and prioritize the set of mirnas supported by high - throughput experiments based on semantic similarities of functional genomic data as previously described ( 27 ) . in brief , this prioritized method ranked candidate mirnas according to their semantic similarity and distance proximity to the gold standard . for lncrnas , we used low - throughput experiments confirmed lncrna as the gold standard and prioritize the set of lncrnas supported by high - throughput experiments based on functional similarity as previously described ( 28 )
. finally , we integrated the tool and the prioritized results into the nsdna database .
nsds are a common group of disorders , encompassing a broad range of clinical features , such as seizures , dementia and paralysis , among others .
researchers and neurologists have been trying to decipher the precise molecular and cellular regulatory mechanisms involved in these disorders . in recent years , emerging work has focused on exploring potential mechanisms involving ncrnas in nsds ( 29 ) .
consequently , nsd  ncrna association data have been accumulating rapidly in this decade . however , most data are dispersed in many independent studies .
it has become increasingly clear that an integrated collection of nsd - associated ncrna data are critical for a complete understanding of nsd processes .
thus , we developed nsdna , an nsd - specific database that provides a comprehensive resource on diverse ncrna  nsd associations across various species . with the advance of disease - related ncrna research ,
several ncrnas ( mainly mirnas and lncrnas ) associated databases were established , such as mir2disease ( 30 ) , hmdd ( 11 ) and lncrnadisease ( 12 ) .
however , to the best of our knowledge , none of these resources were developed to specifically collect experimentally supported nsd 
for example , mir2disease only documents 450 nsd - related entries of associations between 179 mirnas and 26 nsds , while lncrnadisease only includes 196 nsd - related entries of associations between 74 lncrnas and 37 nsds , and only human data are available in these databases .
nsdna contains general nsds , nearly 6 and 4 times the number of nsds than the mir2disease and lncrnadisease databases , respectively .
compared with mir2disease , nsdna also includes data regarding mutations in ncrna and their association with nsds .
for example , in the nsdna database , we also found that a snp ( rs11014002 ) located in mir-603 precursor exhibits a protective effect toward alzheimer disease risk ( 31 ) .
in addition to recording more nsd - ncrna associations , nsdna has several advantages compared to previous studies ( supplementary table s1 ) .
first , nsdna includes many ncrnas categories ( mirnas , lncrnas , sirnas , snornas and pirnas ) , while the above - mentioned databases only include one ncrna category .
second , nsdna includes data for many species , and users can retrieve and download all species or particular species data .
third , data on nsd - associated circulating mirna , treatment - associated mirna and mirna target were also added to the nsdna .
thus , nsdna is a specialized database that provides a comprehensive resource on ncrna and nsd associations .
we believe that nsdna will be beneficial to researchers who wish to further dissect mechanisms involved in nsds .
we found that the number of nsd - associated ncrna articles have generally increased , suggesting that the research on ncrnas may be one of the hottest topics in the nsd field in this decade ( figure 2a ) .
particularly , the number of mirna- and lncrna - related articles have been steadily increasing , and hundreds of mirnas and lncrnas have been identified due to the development of high - throughput technologies . in recent years , new evidence has shown that circulating mirna levels appear stable in fluids and can be used as biomarkers for diagnosis and treatment of nsds ( 32 ) .
therefore , we focused on circulating mirna studies and found that the number of circulating mirnas publications has increased dramatically from 2013 to 2015 , suggesting that circulating mirnas have also become a hot topic in nsd research ( figure 2b ) .
( b ) annual publication counts of circulating mirnas in nsdna . because more than half of the entries were human mirna - associated data , we constructed a human mirna  nsd bipartite network , where nodes represent mirnas or nsds and the lines denote experimentally supported relationships between mirnas and nsds ( supplementary figure s1a ) .
we listed the number of mirnas associated with the nsds , the number of nsds associated with the mirnas , and the five most highly connected nodes ( supplementary figure s1b and s1c ) .
for instance , the disease with the highest connectivity is glioblastoma that is associated with 839 mirnas .
these results indicate that glioblastoma has received wide attention in mirna - related research and also has a complex regulatory mechanism mediated by mirna .
in addition , hsa - mir-146a is the most highly connected mirna node , which is associated with 34 nsds and might be widely associated with nsds .
in conclusion , nsds are common complex disorders that researchers have been trying to understand for years .
nsdna not only provides a comprehensive nsd - specialized database with experimental support but also offers a more global perspective on ncrna functions in nsds . in the future
nsd associations and update the database every two months . in addition , we plan to integrate more sources such as functional annotations and provide additional tools such as an ncrna  nsd association prediction tool .
nsdna will serve as a valuable resource for deciphering ncrna mechanisms and improving the diagnosis and treatment of nsds .
national natural science foundation of china [ 81371324 , 81571166 and 31401090 ] ; specialized research fund for the doctoral program of higher education of china ; foundation of heilongjiang medical science institute ; health and family planning commission of heilongjiang province [ 2016 - 052 and 2016 - 072 ] .
funding for open access charge : national natural science foundation of china [ 81371324 , 81571166 and 31401090 ] .
 the nervous system disease ncrnaome atlas ( nsdna ) ( http://www.bio-bigdata.net/nsdna/ ) is a manually curated database that provides comprehensive experimentally supported associations about nervous system diseases ( nsds ) and noncoding rnas ( ncrnas ) . 
 nsds represent a common group of disorders , some of which are characterized by high morbidity and disabilities . 
 the pathogenesis of nsds at the molecular level remains poorly understood . 
 ncrnas are a large family of functionally important rna molecules . 
 increasing evidence shows that diverse ncrnas play a critical role in various nsds . 
 mining and summarizing nsd  
 ncrna association data can help researchers discover useful information . 
 hence , we developed an nsdna database that documents 24 713 associations between 142 nsds and 8593 ncrnas in 11 species , curated from more than 1300 articles . 
 this database provides a user - friendly interface for browsing and searching and allows for data downloading flexibility . 
 in addition , nsdna offers a submission page for researchers to submit novel nsd  ncrna associations . 
 it represents an extremely useful and valuable resource for researchers who seek to understand the functions and molecular mechanisms of ncrna involved in nsds . 
INTRODUCTION
DATABASE CONSTRUCTION
DATABASE CONTENT
USER INTERFACE
DISCUSSION
CONCLUSION AND FUTURE EXTENSIONS
SUPPLEMENTARY DATA
FUNDING