Bats (order Chiroptera) are one of the largest monophyletic clades in mammals (order Chiroptera), and constitute nearly 20% of living mammalian species. They can perceive their surroundings using a wide range of sensory systems, and have long been regarded as the most unusual and specialized species of all mammals. Most bats are sophisticated echolocators and rely on their echolocation systems for navigation.
However, Old World fruit bats have no laryngeal echolocating ability, and navigate largely by vision. Based on overwhelming molecular genetic evidence, it has been proposed that echolocating bats are paraphyletic (Teeling, et al. 2005) . Old World fruit bats and some laryngeal echolocators (including Rhinolophidae, Hipposideridae, Craseonycteridae, Megadermatidae, and Rhinopomatidae families) are a natural group -the suborder Yinpterochiroptera, and the remaining laryngeal echolocating bats are grouped to another suborder Yangochiroptera .
Two distinct navigation approaches can be employed by echolocating bats: low duty cycle (LDC) echolocation and high duty cycle (HDC) echolocation (Teeling 2009 ). LDC echolocators can separate pulse and echo in time to avoid forward masking, whereas some species of HDC echolocators separate pulse and echo in frequency. It has been documented that rhinolophoid bats might possess the most sophisticated echolocation systems (Jones and Teeling 2006) . Recently, results from some hearing-related genes suggested sequence convergence in laryngeal echolocating bats (Li, et al. 2008; Davies, et al. 2012) . We attempted to investigate whether similar patterns can be detectable in other hearing-related genes. Furthermore, a sensory trade-off between investment in vision and echolocation has been identified (Dechmann and Safi 2009) . Loss-of-function in short-wave sensitive opsin (Sws1 gene) occurred in rhinolophoid bats, which use HDC echolocation and can emit long constant frequency calls (Zhao, et al. 2009 ). Although several bat genomes have been sequenced (Zhang, et al. 2013) , the evolutionary mechanisms of the rhinolophoid bats remains unclear. Comparative genomics will provide us opportunities to investigate whether similar patterns can be detectable in other sensory genes.
The great leaf-nosed bat (Hipposideros armiger) and the Chinese rufous horseshoe bat (Rhinolophus sinicus) are two important species of rhinolophoid bats.
First, these are model organisms with remarkable HDC echolocation ability and can emit continuous ultrasonic calls of long constant frequency with remarkable acoustic features (Doppler-shift compensation) (Schnitzler, et al. 2003) . We can comprehensively explore how rhinolophoid bats evolved a specialized form of echolocation. Second, they are important reservoir hosts of emerging viruses, and the Chinese rufous horseshoe bat has been suggested to carry the direct ancestor of severe acute respiratory syndrome (SARS) coronavirus (Ge, et al. 2013) . In this work, we presented the genomes of the great leaf-nosed bat and the Chinese rufous horseshoe bat using the next generation sequencing platform (Illumina Hiseq 2500). The result revealed the adaptation of auditory sensory perception in HDC echolocators, and showed an extensive contraction of olfactory receptor gene repertoires as well as pseudogenization of some vision-related genes. Furthermore, we performed genome re-sequencing to analyze the population genetic structure of the great leaf-nosed bats.
The genomic data provide genetic evidence of adaptive evolution in rhinolophoid bats.
A female great leaf-nosed bat (Hipposideros armiger) and a female Chinese rufous horseshoe bat (Rhinolophus sinicus) were captured from a cave (N 30°20.497′ Genomic DNA was extracted from bat muscle using the Qiagen DNeasy Blood and Tissue Kit. Six paired-end libraries with insert size of 170 bp, 500 bp, 800 bp, 3k bp, 8k bp and 20k bp were constructed and sequenced for the great leaf-nosed bat and the Chinese rufous horseshoe bat, respectively. The libraries were sequenced using Illumina HiSeq2500 platform, which has a read length of 101 bp. Low quality sequencing reads were filtered out and potential sequencing errors were removed. The following filtering criteria were carried out: 1) Filter reads with >5% unidentified nucleotides; 2) Filter reads with >10 nucleotides aligned to the adapter sequence, allowing <3 mismatches; 3) remove putative PCR duplicates generated by PCR amplification in the library construction process. Finally, we generated 476.5 Gb and 288.5 Gb of sequences for the great leaf-nosed bat and the Chinese rufous horseshoe bat, respectively.
The genome sequences were assembled using ALLPATHS software (Butler, et al. 2008 ). Briefly, contigs were generated by constructing a de Bruijin graph with the sequencing reads from short-insert library data. The graph was simplified to generate the contigs by removing tips, merging bubbles and solving repeats. The sequencing reads were mapped to the assembled contigs, and the scaffolds were constructed by weighting the rates of consistent and conflicting paired-end relationships. At last, we retrieved the read pairs with one end that uniquely mapped to the contigs and the other end located in the gap region, a local assembly for these collected reads was performed to fill the gaps. A more detailed genome assembly method is provided in Supplementary methods.
Total RNAs of the two bats were extracted from brain, cerebellum, heart, liver, stomach, kidney, lung and muscle tissues for the generation of transcriptome data.
Paired-end libraries for RNA sequencing were constructed using the Illumina mRNA-seq Prep Kit. The quality and integrity of the RNA samples were determined using the Agilent2100 Bioanalyzer. Poly(A) mRNAs were isolated using oligo(dT) beads, fragmented, and converted to cDNAs followed by end repair, adaptor ligation, and PCR amplification. The libraries thus generated were sequenced using the Illumina HiSeq2500 platform as described above.
We searched for tandem repeats across the genomes using Tandem Repeats Finder.
Transposable elements were predicted in the genomes by homology search against the known transposable elements (TE) in RepBase (Jurka, et al. 2005 ) (version 20110920) using RepeatMasker version 3.3.0 (Tarailo-Graovac and Chen 2009). The protein-coding genes of the bat genomes were annotated by combining homology-based, ab initio and RNA-seq gene prediction methods. At first, RNA-seq data were assembled using the Trinity package (Trapnell, et al. 2013) . PASA (version r2012-06-25) (Haas, et al. 2003) was then used to map the assembled transcripts.
Based on the set of gene models, a training set was constructed for de novo predictors by selecting the genes with complete structures and at least 100% mapping rate for UniProt vertebrate proteins. For the ab initio prediction, Augustus (Stanke and Waack genes with the training set generated by PASA. For homology-based gene prediction, the protein sequences of human, mouse, dog, cow, little brown bat and large flying fox were downloaded from Ensembl Release 72 and mapped onto the repeat-masked genome using GenBlastA (She, et al. 2009 ). RNA-seq data were mapped to the genome using TopHat (Trapnell, et al. 2009 ), and the transcription-based gene structure were generated by Cufflinks (Trapnell, et al. 2013) . The final gene set was generated by merging all genes predicted using GLEAN software (http://sourceforge.net/projects/glean-gene/). To infer gene function, it was based on the best match of the alignment to the Swissprot and translated EMBL nucleotide sequence data library databases using BLASTP. InterproScan (Mulder and Apweiler 2007 ) was used to determine motifs and domains in the final gene set. To evaluate completeness of the genomes and annotations, CEGMA method (Parra, et al. 2007) was employed.
We used the TreeFam methodology (Li, et al. 2006) to define gene families in 14 mammalian genomes (human, macaque, mouse, rat, dog, cat, horse, rhinoceros, cow, pig, little brown bat, large flying fox, great leaf-nosed bat and Chinese rufous horseshoe bat). The protein sequences of other 12 mammalian species were obtained from Ensembl database (Release 72). Gene family expansion and contraction analysis was performed by CAFÉ software (De Bie, et al. 2006) . A random birth and death model was proposed to study gene gain and loss in the gene families across a user-specified phylogenetic tree. A global parameter λ (lambda), which described both gene birth (λ) and death (μ = -λ) rates across all branches of all gene families was estimated using the maximum likelihood method. A conditional p-value was calculated for each gene family, and families with conditional p-values less than 0.05 were considered to have a significantly accelerated rate of expansion and contraction.
Protein sequences of the aforementioned 14 mammals were aligned using MUSCLE software (Edgar 2004) . All orthologous genes were concatenated to one super gene for each species. RAxML (Stamatakis 2014 ) was applied to build phylogenetic trees. We partitioned the data by coding genes, and evaluated the model parameter independently for each partition. In all partitioned analyses, the empirical base frequencies and the evolutionary rates were estimated independently for every partition. Bootstrap support was obtained by repeating the original partitioned ML RAxML analysis on 100 bootstrap replicates for each dataset using different random number seeds in each repetition. Next, we inferred the species tree using coalescent method: maximum pseudo-likelihood estimation of species tree (MP-EST) (Liu, Yu, et al. 2010) . Individual gene tree for each gene was estimated using the maximum-likelihood method and rooted by an outgroup (human). Species trees were estimated from the rooted gene trees in the program MP-EST with 100 bootstrap replicates. The results supported that bats are member of scrotifera (Chiroptera + Carnivores + Perissodactyla + Cetartiodactyla) with bat lineage diverging from Fereuungulata (Carnivores + Perissodactyla + Cetartiodactyla).
The values of Ka, Ks, the Ka/Ks ratio were estimated for each gene using the Codeml programs nested in the PAML package (Yang 2007) . In order to detect positively selected genes, optimized branch-site likelihood model (Zhang, et al. 2005) was used. We separately explored the positively selected genes in the great leaf-nosed bat and the Chinese rufous horseshoe bat. For each analysis, only one bat species was selected as foreground branches, and all other species were regarded as the background branches. The revised branch-site model A was employed, which attempts to identify positive selection acting on some sites on the "foreground branches".
Using an likelihood ratio test (LRT), the alternative hypothesis that positive selection occurs on the foreground branches (Ka/Ks > 1) is compared with the null hypothesis (Ka/Ks=1). Bayesian empirical Bayes values were used to identify sites under positive selection. Then, branch two-ratio model was applied to detect accelerated evolved genes in specific lineage. The one-ratio model assumed an equal Ka/Ks ratio for all lineages in the phylogeny, and the two-ratio model assumed two Ka/Ks ratios: one branch for the background, one for the foreground branch leading to the specific species. Then, Clade model C was employed to test for positive selection along the rhinolophoid bats. The two clades were assumed to share sites under purifying selection and neutral evolution, but to differ at a third site partitions under divergent selection. The null model used for the Clade model C was M2a_rel (Weadick and Chang 2012) , whose LRT has a relatively lower false-positive rate. GO annotations were downloaded from Ensembl databases and were assigned to these orthologous genes. The binomial test was used to identify GO categories with more than 20 gene that had an excess of non-synonymous changes in bat lineages. Next, we used the program MAPP (multivariate analysis of protein polymorphism) (Stone and Sidow 2005) to evaluate the physicochemical impact of these convergent amino acid substitutions on bats. Physicochemical variations can be used to predict how these particular convergent amino acid substitutions might affect protein function.
In this work, we performed a probabilistic analyses of the sequence convergence in echolocating bats. A maximum likelihood approach, implemented in the software package Codeml ancestral, was used. We compared the pair-wise branches of two echolocating bat in the phylogeny, and posterior probabilities of all possible amino acid substitutions were calculated. The probabilities of divergent and convergent substitutions were calculated as the sum of joint probabilities of substitutions between the two branches of echolocating bats. Convergence and divergence estimates were based on posterior distributions of ancestral states and substitutions. The same state (same amino acid) represents convergent substitutions, and the different state represents divergent substitutions. Finally, to further validate that the convergence between two branch pairs of echolocating bats was significant, we performed the simulation analysis to compare the observed probabilities against that of the null hypothesis. Simulated sequences were generated using Evolver, another package from PAML package (Yang 2007) . The branch-wise convergence probabilities were calculated with 1,000 replicates.
We used the similar in silico method as previously reported in Dong et al. (Dong, et al. 2009 ). At first, we used previously published OR genes in vertebrates as query sequences (Niimura and Nei 2007) and conducted a TBlastN search against the genome sequences with a cutoff E value of 1e-10 to identify the OR gene repertoires.
Here, we totally identified OR gene repertories from eight mammalian genomes (the Site (http://genome.ucsc.edu). Then, the non-redundant blast-hits were extended to the 5' and 3' directions along the genome sequences, and the potential coding regions were extracted from these sequences. The chemosensory receptor genes in mammals have high sequence similarity. Here, we re-performed a TBlastN against the genome sequences using OR coding genes identified from each species, and the non-redundant blast-hits were used to identify the OR pseudogenes containing interrupting stop codons or frameshifts. To identify partial OR genes from these sequences, we extracted the sequences that did not have any nonsense or frameshift mutations. We then constructed a multiple alignment of these sequences together with functional OR genes by the program E-INS-i in MAFFT version 5.8 (Katoh, et al. 2002) . From those alignments, we extracted partial OR sequences that meet the following criteria. When the C-terminal region of an OR gene is missing from the genome sequence, the N-terminal region should contain an initiation codon at a proper position and should not contain any nonsense mutations, frameshifts, or long gaps. When the N-terminal region is missing, the C-terminal portion should have a stop codon at a proper position and should not contain any nonsense mutations, frameshifts, or long gaps. We also identified 6 and 10 sequences with nonsense stop codon in the great leaf-nosed bat and Chinese rufous horseshoe bats, which miss both a start and stop codons.
However, These sequences were removed because they have relatively short sequence length (~400 bp) and have strong sequence similarity with bitter taste receptor genes.
To assign identified OR genes into distinct OR gene families, a collection of protein sequences from HORDE database version 42 (Safran, et al. 2003 ) was used.
To detect the extensive gain and lose of OR gene repertories, we employed the reconciled tree method (Nam and Nei 2005) , in which the topology of a gene tree is reconciled with that of a species tree. An in-house program was applied. Briefly, based on the phylogenetic tree of OR genes, we compared the condensed gene tree and the species tree under the parsimony principle. The number of ancestral genes can be estimated, and the information of the past occurrence of gene expansion and contraction. Here, we used a 70% condensed tree of OR genes for analyses.
A list of vision-related genes were obtained from GO category of visual perception (GO:0007601). We subjected human vision-related proteins to TblastN against the genomes with cutoff threshold of e-value 1e-5. We found that best-hits for each human protein by using the criteria that more than 30% of the aligned sequences showed an identity above 30%. GeneWise algorithm was employed to identify potential pseudogenes with parameters -genesf -for -quiet. Those genes with frame shifts or pre-mature stop codons were considered as candidates. We then filtered them as follows: 1) we aligned all human proteins to their corresponding genomic loci, and those genes with frameshifts or premature stop codons in human-to-human alignments were removed; 2) as for the human-to-human alignments, those genes with obvious splicing errors near their frameshifts or premature stop codons were removed; 3) candidate pseudogenes with a low number of sequencing reads covering their frameshift or premature stop codon sites were regarded assembly error. Those genes with a number of reads containing genotype variations at these sites were considered as heterozygous and were also removed.
We used a method based on Ka/Ks to identify GO categories that significantly above average in the great leaf-nosed bat genome and Chinese horseshoe bat genome.
At first, the Ka and Ks rates are calculated by PAML package from all aligned bases with quality score larger than 20 in orthologs, using the F3x4 codon frequency model and the REV substitution matrix. In order to examine the evolution function catalog, we downloaded the GO annotation of human gene from the Ensembl biomart database (release-71). We estimated the average Ka and Ks values for those genes which have annotated GO as following equations (S1, S2).
Where T is the number of annotated genes within GO categories, i a and i A are the numbers of non-synonymous substitutions and sites, i s and i S are the numbers of synonymous substitutions and sites in gene i, as estimated by PAML, respectively.
The expected proportion of non-synonymous substitutions A p in a GO category was then calculated (S3).
For a given GO category C, the probability c p of observing an equal or higher number of non-synonymous substitutions and synonymous substitutions was calculated assuming a binominal distribution (S4).
Where C a and C s are the total number of non-synonymous and synonymous substitutions in GO category C, respectively.
We applied an approach to the binomial test described above to identify GO categories that have an excess of non-synonymous changes on one lineage. For lineages x and y, the average proportion of non-synonymous substitutions were calculated by the following formula (S5).
x is the total number of non-synonymous substitutions in the x lineage, y is the total number of non-synonymous substitutions in the y lineage, and the divergence of the proportion of non-synonymous substitution numbers in different lineages between the observed and expected obeys binomial distribution, the formula is as in the following equation (S6).
As described for the absolute rate tests, we then computed this statistic for every GO category, as well as for every category in 10,000 randomly permuted data sets.
We sampled a total of 20 great leaf-nosed bats distributed in four different locations. Genomic DNA was extracted from wing membranes of each individual.
Paired-end sequencing library with an insert size of 500 bp was constructed for each sample, and sequenced on the Illumina Hiseq 2500 platform with 2×101 bp mode.
Duplicate sequencing reads were filtered out according to the following criteria: 1) any reads with >10% unidentified nucleotides; 2) reads with >10 nt aligned to the adapter sequence, allowing <10% mismatches; 3) reads with 50% bases having phred quality <5. The filtered reads were mapped to the genome using BWA, and SAMtools were used to call SNPs. Then, we filtered SNPs using VCFtools and GATK under the following criteria: 1) coverage depth >4 and <10000; 2) root mean square mapping quality >10; 3) the distance of adjacent SNPs >10 bp; 4) the distance to a gap > 10; 5) read quality value >30.
To estimate phylogenetic relationships, the genetic distances were calculated among all samples to generate a neighbor-joining (NJ) tree using PHYLIP. We performed a principal component analysis using the package GCTA. The population structure was inferred using frappe (v1.1) with a maximum likelihood method (Tang, et al. 2005) .
Sliding-window approach (10 kb window sliding in 10 kb step) was employed to quantify polymorphism levels (θ π , pairwise nucleotide variation as a measure of diversity) and genetic differentiation (Fst) between the high altitude region (DQ) and low altitude regions (TW, JX and GZ). To detect significant signatures of selective sweep, Z-transformed Fst values was calculated.
Next-generation genome sequencing was carried out, generating 476.5 Gb and 288.5 Gb of sequences for the great leaf-nosed bat and the Chinese rufous horseshoe bat (Supplementary Table S1 ), respectively. The genome size was estimated to be 2.18
Gb and 2.07 Gb for the great leaf-nosed bat and the Chinese rufous horseshoe bat ( Supplementary Fig.1 Supplementary Fig.3 ).
Known transposon-derived repeats account for 25.8% and 28.5% of the genomes in the great leaf-nosed bat and the Chinese rufous horseshoe bat, respectively, which are lower than other non-bat mammalian species (Supplementary Table S5 ). To facilitate the genome annotation, we generated a high-depth transcriptome data from these two rhinolophoid bats. With repeats masked, the genome was annotated by integrating the homologous prediction, ab initio prediction and transcription-based prediction methods. As a result, a non-redundant reference gene set of 22,009 and 23,152 protein-coding genes were generated for the great leaf-nosed bat and the Chinese rufous horseshoe bat ( Supplementary Fig.4) , respectively. We employed CEGMA method to evaluate the completeness of genome annotation. The result showed that the vast majority of the core genes were present in our predicted gene sets (97.08% for the great leaf-nosed bat and 96.14% for the Chinese rufous horseshoe bat), indicating the completeness of gene sets identification. Next, we aligned the transcriptome sequencing reads to the predicted genes, and the result showed that approximately 96% of exons are accurately covered (96.8% for the great leaf-nosed bat and 97.1% for the Chinese rufous horseshoe bat). Comparative analysis showed a high gene sequence similarity between them (91%, Supplementary Fig.5 ).
We next examined the level of homology between our predicted genes and sequences in the Uniprot database. The result showed that >92% of the genes were functionally annotated (94% for the great leaf-nosed bat and 92.2% for the Chinese rufous horseshoe bat).
Compared with the gene families in other three mammalian species -the little brown bat, large flying fox and human, we identified 8,792 homologous gene families shared by five species. A total of 975 gene families were specific to the rhinolophoid bats ( Fig. 1) . Further functional annotation indicated that the rhinolophoid bats specific gene families were significantly over-represented in two major functional categories: ATP binding (43 genes, F.D.R.= 0.0002) and immunity and host defense (25 genes, F.D.R.= 0.0051; Supplementary Table S6) .
Until now, the relationship of bats to other members of superorder Laurasiatheria has proven difficult to resolve. Some studies insisted that bats belong to the clade of Pegasoferae which comprises chiroptera, carnivores and odd-toed ungulates (Lindblad-Toh, et al. 2011; Meredith, et al. 2011; McCormack, et al. 2012) , whereas others proposed that bats are a sister group to the clade comprising carnivores and euungulata (Pumo, et al. 1998; Murphy, et al. 2001; Murphy, et al. 2007; Song, et al. 2012; Zhang, et al. 2013) . To determine the phylogenetic position of bats within the superorder Laurasiatheria, a total of 4,569 single-copy 1:1 orthologous genes were Fig.6 ). The result based on nucleotide data was in line with previous analysis that bats are a sister group to odd-toed ungulates, whereas the result based on amino acid data supported that bat bats are sister group to the Fereuungulata (Carnivores + Perissodactyla + Cetartiodactyla). To account for the tree discordance among loci, coalescent method was applied. Coalescent trees were highly consistent with the result inferred from amino acid data using partitioned method ( Supplementary Fig.7) . To dissect the phylogenetic signal, previously published eight different phylogenetic hypotheses ( Supplementary Fig.8) were proposed (Waddell, et al. 1999; Murphy, et al. 2001; Nishihara, et al. 2006; Prasad, et al. 2008; Lindblad-Toh, et al. 2011; Meredith, et al. 2011; McCormack, et al. 2012 Table S7 , Supplementary Fig.9 ). The result is consistent after incorporating the data from Eulipotyphyla group ( Supplementary Fig.10) . We subsequently estimated the divergence time among these 14 mammalian species. The bat lineage seems to be diverged from Fereuungulata around 81 million years ago, and the rhinolophoid bats seem to be diverged from the Old World fruit bats around 68 million years ago.
Comparative genome analyses were carried out to assess the evolution and innovation within the rhinolophoid bats. We next determined the expansion and contraction of gene orthologous clusters during evolution. The result identified 48 significantly expanded and 65 significantly contracted gene families in the great leaf-nosed bat, 46 significantly expanded and 54 significantly contracted gene families in the Chinese rufous horseshoe bat (Fig. 2) . Functional annotation showed that gene family contraction mainly included many olfactory receptor gene families in both rhinolophoid bat lineages (Supplementary Table S8 ), which is consistent with the result that the olfactory system is aberrant in some echolocating bats. Many of the expanded gene families in both rhinolophoid bats are significantly enriched in immune-related functional categories (Supplementary Table S9 ). Moreover, we identified 577, 453 and 182 positively selected genes in the great leaf-nosed bat, the Chinese rufous horseshoe bat and the large flying fox, (Supplementary Tables S10, 11, 12), respectively.
Olfaction is of great importance in the lives of bats. Many bats can use olfaction for mother-pup recognition, find food and avoid danger. In Old World fruit bats, olfaction appears to be of particular importance, and fruit bats can find food from scent cues. Animals that rely heavily on the sense of smell tend to have large numbers of OR genes, while species that always use other senses have fewer functional OR genes (Niimura and Nei 2007) . It has been suggested that bats displayed a diverse olfaction abilities. In order to describe the diversity of bat OR gene repertoires, we identified the entire set of OR genes of four bat species (Supplementary methods, Supplementary Table S13 ). In line with previous work (Hayden, et al. 2014) , we observed that echolocating bats have less fraction of OR pseudogenes (18% for the great leaf-nosed bat, 16% for the Chinese rufous horseshoe bat and 14% for the little brown bat) than non-echolocating bats (26% for the large flying fox). However, further analysis showed that the large flying fox and little brown bat have more than 400 intact OR genes while these two rhinolophoid bats only have <300 intact OR genes. This finding is consistent with the result that rhinolophoid bats have a relatively small olfactory epithelium than the frugivorous Pteropodidae (Neuweiler 2000) . Next, we reconstructed a protein neighbor-joining tree of all newly identified intact OR genes in bats (Fig. 3a) . It is obvious that OR genes can be classified into two distinct classes based on sequence similarity: Class I, postulated to bind to water-borne molecules, and Class II, hypothesized to bind to airborne molecules. The exact number of OR genes in each class/OR family are shown (Supplementary Table   S14 , Table S15 ). It seems that four bat species contain similar number of OR genes in Class I, while OR gene contraction occurred in two rhinolophoid bats in Class II .
Previous works have documented that the number of OR genes varies extensively among mammalian species, and extensive gains and losses of OR genes have been observed (Niimura and Nei 2007) . To further understand the evolutionary changes of OR gene repertoires, we estimated the gains and losses of the OR genes in a diverse range of mammals (Supplementary methods). Evolutionary changes in the number of OR genes in mammals have been shown in Fig. 3b . We can clearly identify an extensive OR gene contraction events occurred to the branch leading to the common ancestor of bats. Further extensive gene contractions can be observed in the branch leading to the rhinolophoid bats. This finding also suggests massive "birth-and-death" of OR genes in the bat species. Table S18 ). Since high omega may be due to stochastic effect caused by extremely small sample size, we removed these genes with omega value of 999. The result is also stable that more positively selected genes were detected in the branches leading to echolocating bats (12 genes, great leaf-nosed bat, P = 9.1E-5; 10 genes, Chinese rufous horseshoe bat, P = 8.8E-3; 10 genes, little brown bat, P = 0.011). Next, branch model (two-ratio model) was carried out with the attempt to detect genes with accelerated evolution in the bat species. The result further indicated that more hearing-related genes have higher ɷ values on the branches leading to echolocating bats than all other lineages (Supplementary Table S19) . Clade model C implemented in PAML was employed (Weadick and Chang 2012) , and the result also persisted that more positively selected genes were detected in the branches leading to echolocating bats (Supplementary Table S20 ). Moreover, a significant association between the average number of non-synonymous substitutions for all the hearing-related genes leading to each mammalian species and the estimated frequency of best hearing sensitivity for that species (r = 0.84, P = 0.00032, Fig. 4 ) was observed. No significant correlation between such hearing frequencies and number of synonymous changes was observed (P = 0.132). A significant association between the number of non-synonymous changes between sister taxa was observed (r = 0.67, P = 0.006). It is obvious that echolocating bats have typically undergone many more non-synonymous changes in the hearing-related genes than non-echolocating mammals. These results indicated the evolution of ultrasonic hearing in the rhinolophoid bats has involved in adaptive amino acid replacements in the hearing-related genes, which provided evidence conferring greater auditory sensitivity to ultrasonic frequency.
Previous works have documented that seven hearing-related genes underwent convergent evolution in echolocators (Li, et al. 2008; Liu, Cotton, et al. 2010; Davies, et al. 2012; Shen, et al. 2012 ). Here, genome-wide signatures of convergent evolution were examined in laryngeal echolocating bats.
Except for the previously reported seven hearing-related genes, we totally identified 10 genes examined in the sound of perception category containing potential sequence convergent loci (site-wise convergence posterior probabilities > 0.5). To confirm our result, we amplified and sequenced these 10 hearing-related genes from another two echolocating bats (Eptesicus fuscus and Miniopterus natalensis). The result also showed that these 10 genes have higher convergence probabilities occurred in echolocating bats from a wider range of taxa, and the convergence probabilities between branches were significant based on simulations (Supplementary Table S21 ).
However, maximum likelihood trees recovered the topology that all laryngeal echolocating bats formed a monophyletic clade for only four genes (COL1A1, ICAM1, BSND and STRC, Supplementary Fig.11 ). Further analyses showed that echolocating bats are paraphyletic based on synonymous substitutions, whereas the non-synonymous trees revealed monophyly of laryngeal echolocators for only one hearing-related genes (STRC gene, Supplementary Fig.12 ). Next, multivariate analyses of protein polymorphism (MAPP) was employed to detect the physicochemical impact of convergent substitutions in echolocating bats. MAPP scores were estimated for the amino acid variants nested in the STRC gene, and the result showed that these replacements had important functional effects (MAPP score = 18.61, P = 1.44E-4 for H28Q; MAPP score = 10.33, P = 3.98E-3 for A39T; MAPP score = 7.37, P = 2.27E-2 for V169I). We further measured the number of sites with convergent amino acid substitutions along the branches as a direct measurement of sequence convergence, and found that the number of convergent sites in the branch pairs is proportional to the number of divergent sites ( Supplementary Fig.13 ). The number of convergent sites in the laryngeal echolocating bats does not significantly exceed that between the branch pair of the little brown bat and large flying fox, given their numbers of divergent sites (Supplementary Table S22 ). No significant differences was observed in the total number of sites that have experienced convergent substitutions from hearing-related genes. This result indicated that there is no exceptional genomic signature indicative of adaptive convergence between laryngeal echolocating bats, and genes with adaptive convergent substitutions might confine to few specific genes.
Bats are nocturnal mammals. The eyes of most echolocating bats are relatively small and poorly developed, whereas Old world fruit bats often have excellent eyesight . Rhinolophoid bats have the most sophisticated echolocation ability, and have been proposed that some genes involved in visual perception may have undergone relaxed selection (Zhao, et al. 2009 ). We next examined the molecular basis for the poor visual perception in the echolocating bats.
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Bats have long been regarded as important reservoir hosts of emerging viruses (Calisher, et al. 2006) . 
To examine population dynamics and understand evolutionary processes, we sampled 20 great leaf-nosed bats from 4 major distributed locations in China, including one group from high-altitude region (Fig. 6a, Table S28) are located at the intergenic regions.
In order to resolve their phylogenetic relationships, we constructed a neighbor-joining (NJ) tree based on pairwise genetic distances (Fig. 6b) . This result showed that the great leaf-nosed bats formed separate groups according to the different locations. Principal component analysis clearly divided these samples into four groups (DQ, GZ, JX and TW, Fig. 6c) . These results suggested that there were significant population structures among the great leaf-nosed bat populations.
Furthermore, we performed population structure analysis. When K=4, all these four populations were clearly separated (Fig. 6d) .
Next, we measured the genetic diversity values (θ π ) of four populations, and found similar sequence diversity values (DQ: 0.0012, GZ:0.0009, JX:0.0009 and TW:0.0011, Supplementary Fig. S14 ). We further observed that the population differentiation statistic (Fst) between populations, and the result showed little differentiation among populations (Fst ranging from 0.013 between JX and TW to 0.057 between TW and DQ, Supplementary Table S29) , which suggests universal inter-region gene flows.
Since the method of population differentiation has been widely used to detect selective sweeps (Akey, et al. 2010; Axelsson, et al. 2013; Gou, et al. 2014 Table   S31 ). The result showed that genes related to catabolic process are likely to have been targets of recent positive selection. Interestingly, we found that five genes (EPAS1, PLXND1, GJA1, SELL and CHDH) belong to hypoxia response related GO categories (Pugh and Ratcliffe 2003; Storz and Moriyama 2008) , including 'angiogenesis', 'blood coagulation', 'blood vessel morphogenesis' and 'oxidoreductase activity'.
EPAS1 can respond to the changes in available oxygen in the cellular environment under the high-altitude conditions. Our work suggested that EPAS1 is involved in a selective sweep during the move of bats from low to high altitude. Although hypoxia GO categories are not over-represented, these highlighted hypoxia-related genes gave us a clue that genetic adaptations might be associated with high altitude.
Using deep sequencing and de novo assembly, we generated two genomes of rhinolophoid bats. Rhinolophoid bats can perceive the world by using a wide range of sensory mechanisms, some of which have become highly specialized. These genome data provided useful resources to decipher the molecular adaptations of phenotypic traits.
Rhinolophoid bats arguably possess the most sophisticated echolocation systems, and can emit relatively long calls adapted to detect and classify the wing beats of insects. They are heavily reliant on hearing for a variety of ecologically important roles. Previous works have documented that hearing-related genes are predominantly evolutionarily conserved in mammals (Kirwan, et al. 2013) . Here, we found evidence that some hearing-related genes have undergone Darwinian selection associated with the evolution of specialized constant frequency echolocation. Positive selection acting on hearing-related genes in rhinolophoid bats might result from the extreme selectivity used in auditory processing by these bats. Many previous works have reported the sequence convergence of some hearing-related genes reuniting echolocating bats (Li, et al. 2008; Liu, et al. 2011; Davies, et al. 2012; . We found no genome-wide sequence convergence for echolocation, indicating erroneous phylogenetic grouping are still rare It has been suggested that the enlargement of one area of brain might be associated with the reduction in size of other brain area (Harvey and Krebs 1990) . The auditory cortex and the inferior colliculus are extremely enlarged in the volume in laryngeal echolocating bats (especially in rhinolophoid bats), while visual brain areas are relatively enlarged in Old World fruit bats (Dechmann and Safi 2009 ). The trade-off has been proposed in investment in brain tissues because of the extreme energetic demands imposed by neural processing. Our result showed more visual perception genes have become pseudogenes in rhinolophoid bats, and it is reasonable to speculate that some visual perception gene may have undergone relaxed natural selection in echolocating bats. Meanwhile, positive selection acting on some hearing-related genes was identified. Such concordance suggests that some genes are impacted by natural selection, which raised the possibility that changes at the sensory genes will have direct consequences for those genes controlling for other sensory modalities, perhaps via trade-offs. This finding supports the longstanding but weakly supported assumption that bats are experiencing trade-off between vision and audition .
Olfaction is of great importance in the lives of bat species. Previous works have identified olfactory receptor (OR) gene repertoire in the little brown bat and the large flying fox using the profile hidden Markov model (Hayden, et al. 2010; Hayden, et al. 2014 in specific gene family. A possible explanation is that the little brown bat has no well-developed olfaction ability, but tends to recognize specific odorants after recent OR gene duplication. These comparative analyses have provided great insights into adaptation to their specialized sensory mechanisms.
In this work, we re-sequenced the genome of 20 great leaf-nosed bats from four distributed locations. The genome re-sequencing analysis has been performed based generally on the following considerations: 1) to characterize the genetic diversity and patterns of evolution; 2) to understand the genetic bases of adaptation to high altitude in the great leaf-nosed bats. Efforts for the conservation measures will benefit from the knowledge of population genetic structure of the great leaf-nosed bats. Here, we found very little differentiation among populations, which suggests universal inter-region gene flows or incomplete lineage sorting. A broader geographical scale analysis is needed in the future. Furthermore, we provided evidence of genetic adaptation in the great leaf-nosed bat that are associated with high altitude. Selective sweep mapping was conducted for populations from different altitudes, and identified several hypoxia-related genes with a high extent of differentiation on the genome scale. EPAS1 is transcription factor that respond to the changes in the available oxygen in the cellular environment under high-altitude conditions, and mutations at EPAS1 are tightly associated with hematologic phenotypes (van Patot and Gassmann 2011). Previous works have documented that EPAS1 polymorphisms are associated with Tibetan people with lower hemoglobin concentrations (Beall, et al. 2010) . A loss-of-function role of EPAS1 might exist in high-altitude adaptation. So, our result indicated potential high-altitude hypoxia adaptation mechanisms of the great leaf-nosed bat. Our work is based on a limited genome re-sequencing resource, and data from more samples are necessary for future work. However, false positives notwithstanding, the results provided valuable staring points for experimental follow-up, and suggested an initial evolutionary scenario of bats in adaptation to high-altitude hypoxia.
To the best of our knowledge, it is the first time to report the de novo assembled genome and genome re-sequencing of bats with long constant frequency echolocation calls. These data are essential for us to understand the evolution of bats. 