Database

Database generation

To generate the ranking for a given quality attribute (for example ‘total number of reads’) we first obtain the corresponding values of all samples from a reference corpus. Note that we use different reference corpus: same kingdom, same sample protocol, same sample species and same species and protocol. The obtained values are sorted into ascending order and the corresponding percentile of the observed value is calculated.

Therefore, each variable can hold desired values either when the percintiles are low (percentage of adapter-dimers or ribosomal RNA for example) or when they are high (number of reads or detected microRNAs). Either way, the quartile-based colour code is always asigned taking the nature of the feature into account, i.e. the ‘best’ quartile will always be displayed in green while the ‘worst’ quartile will be red.

To represent the heatmap we had to make all percentiles coherent as the colors are programmatically filled. To achieve this, we internally calculate (100-Percentile) for those attributes for which high percentiles indicate good quality.

Database contents

Species	Samples	Studies	total number of raw reads
Human	17745	850	2.32244E+11
Mouse	7303	460	1.01064E+11
Arabidopsis thaliana	1863	199	40669870610
Bos taurus	1753	82	23052849107
Rattus norvegicus	1662	80	13839004606
Drosophila melanogaster	1514	163	36856287588
Caenorhabditis elegans	1255	99	21744837798
Sus scrofa	751	80	9338528047
Zea mays	603	52	14216151487
Equus caballus	480	21	6354331280
Danio rerio	268	32	4146066655
Solanum lycopersicum	244	47	4169714559
Gallus gallus	242	35	3708335610
Aedes aegypti	232	24	4514242092
Canis lupus familiaris	165	11	1869150079
Apis mellifera	71	13	827627067
Oryctolagus cuniculus	54	8	556118648
Other	133		3919986171