**6. New high-throughput technology for drug repositioning**

Researchers try to develop new high-throughput RNASeq technology to improve the precision of transcriptome with the constraint of cost. For example, the microarray was used in the first version of CMap, while the L1000 technology was used in the second version of CMap, that is LINCS with a more than 1000-fold scale-up of the CMap. Via a Luminex bead-based probe hybridization, the L1000 only measured the mRNA abundance of 978 "landmark" genes with the expression of the remaining gene inferred by a machine learning algorithm [9]. This selection largely resulted from lowering the cost of obtaining the transcriptome of a huge scale of drugs and compounds.

RNA-Seq via Next-Generation Sequencing is a relatively new emerging technology in the drug repositioning field. Due to the higher cost, researchers tried to maintain the transcriptome performance when lowering the cost in several ways. For example, a subset of genes with a reduced representation of the transcriptome could be sequenced instead of all the mRNA. The L1000 technology used the most informative genes, named "landmark" genes [9]. Deepak et al. argued that a knowledge-driven subset of 1500 sentinel genes could precisely predict pathway perturbations [63]. RASL-seq (RNA-mediated oligonucleotide annealing, selection, and ligation) only measured hundreds of pre-defined genes in response to a set of 350 chemicals and their mixtures, which provided a cost-effective approach to quantify gene expression signature with a panel of marker genes [64]. TempO-Seq, Templated Oligo assay with Sequencing readout, could determine the whole transcriptome via a targeted way, requiring less sequencing depth [65].

The pooled and low-depth Next-Generation Sequencing is another approach to lower the cost but maintain the performance. PLATE-seq (pooled library amplification for transcriptome expression) introduced the sample-specific barcodes, allowing pooled library construction in 96 wells and low-depth sequencing, which is about 15-fold less expensive than canonical RNA-Seq [66]. DRUG-seq efficiently captured transcriptional changes with low-depth reads by importing cell barcode

and Unique Molecular Index (UMI) in 384- and 1536-well format with fewer steps, compared with PLATE-seq [67]. Notably, DRUG-seq also supplied an open-source R program analysis pipeline at Github recently [68]. BRB-seq (Bulk RNA Barcoding and sequencing) used early-stage multiplexing to produce 3′ cDNA libraries for multi-samples, while with a lower cost [69]. 3'Pool-seq was an optimized cost-efficient method of transcriptome profiling, which was also adapted for a 96-well plate format and ERCC spike-ins. Collectively, researchers have developed multi new transcriptome technologies while lowering the cost of sequencing to implement the RNASeq for large-scale samples, which could be due to the different doses, different treatments, and different periods of treatment.

Other types of gene signatures, such as the proteome and metabolome, could also be used in drug repositioning. Zhao et al. created a systematic map of proteindrug connectivity that compiled 210 clinically relevant protein signatures based on antibody-based proteomics technology in more than 12,000 cell-line samples in response to about 150 drugs [70]. ProTargetMiner was a proteome signature library of 56 molecules in A549 cancer cell lines, forming a valuable tool in drug discovery [71]. Benjamin et al. profiled the proteomes of five lung cancer cell lines (such as A549, Calu6 and Calu1) perturbed by more than 50 drugs based on the label-free proteomics platform [72]. Moreover, an atlas (http://bbmri.researchlumc.nl/ atlas/) of 87 drugs and 150 clinically relevant plasma-based metabolite associations will contribute to the drug development as well [73]. Other omics data, besides transcriptome, related to drugs and diseases will promote the drug repositioning flourishing. In summary, new omics technology will precisely quantify the signatures related to drugs and diseases with a low cost, permitting the large-scale omics project, enlarging the searching library for drug repositioning.
